meshterms.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Scrape all the mesh terms from the pubmed papers\n",
    "\"\"\"\n",
    "import math\n",
    "import urllib\n",
    "\n",
    "from bs4 import BeautifulSoup\n",
    "from contextlib import closing\n",
    "import cxutils as cx\n",
    "import matplotlib.pyplot as plt\n",
    "from requests import get\n",
    "from requests.exceptions import RequestException\n",
    "\n",
    "# Too old, need to figure out a replacement\n",
    "# from wordcloud import WordCloud\n",
    "# %matplotlib qt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# List of paper titles found on pubmed after removing FPs.\n",
    "titles = cx.read_lines(os.path.join('search_results', 'pubmed', 'with-constraints.txt'))\n",
    "\n",
    "base_url = 'https://www.ncbi.nlm.nih.gov/pubmed/?term='"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The base page with search results. This is uncleaned (false positives).\n",
    "# scrape_page = 'https://www.ncbi.nlm.nih.gov/pubmed/?term=(mimic-ii+OR+mimic-iii+OR+mimicii+OR+mimiciii+OR+mimic-2+OR+mimic-3+OR+mimic2+OR+mimic3)+AND+(physionet+OR+icu+OR+%E2%80%9Cintensive+care%E2%80%9D+OR+%E2%80%9Ccritical+care%E2%80%9D)'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def simple_get(url):\n",
    "    \"\"\"\n",
    "    Attempts to get the content at `url` by making an HTTP GET request.\n",
    "    If the content-type of response is some kind of HTML/XML, return the\n",
    "    text content, otherwise return None.\n",
    "    \"\"\"\n",
    "    try:\n",
    "        with closing(get(url, stream=True)) as resp:\n",
    "            if is_good_response(resp):\n",
    "                return resp.content\n",
    "            else:\n",
    "                return None\n",
    "\n",
    "    except RequestException as e:\n",
    "        log_error('Error during requests to {0} : {1}'.format(url, str(e)))\n",
    "        return None\n",
    "\n",
    "def is_good_response(resp):\n",
    "    \"\"\"\n",
    "    Returns True if the response seems to be HTML, False otherwise.\n",
    "    \"\"\"\n",
    "    content_type = resp.headers['Content-Type'].lower()\n",
    "    return (resp.status_code == 200\n",
    "            and content_type is not None\n",
    "            and content_type.find('html') > -1)\n",
    "\n",
    "\n",
    "def log_error(e):\n",
    "    \"\"\"\n",
    "    It is always a good idea to log errors.\n",
    "    This function just prints them, but you can\n",
    "    make it do anything.\n",
    "    \"\"\"\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Title: prediction model of in-hospital mortality in intensive care unit patients with heart failure: machine learning-based, retrospective analysis of the mimic-iii database\n",
      "Number of items: 0\n",
      "Title: effects of circadian heart rate variation on short-term and long-term mortality in intensive care unit patients: a retrospective cohort study based on mimic-ii database\n",
      "Number of items: 0\n",
      "Title: the impact of transthoracic echocardiography on the short-term prognosis of elderly patients in the intensive care unit: a retrospective analysis based on the mimic-iii database\n",
      "Number of items: 0\n",
      "Title: a comprehensive evaluation for the prediction of mortality in intensive care units with lstm networks: patients with cardiovascular disease\n",
      "Number of items: 0\n",
      "Title: risk factors for death and their predictive value on diabetic kidney disease patients in intensive care unit based on mimic-iii database\n",
      "Number of items: 0\n",
      "Title: outcomes for patients with sepsis following admission to the intensive care unit based on health insurance status: a study from the medical information mart for intensive care-iii (mimic-iii) database\n",
      "Number of items: 0\n",
      "Title: dynamic survival prediction in intensive care units from heterogeneous time series without the need for variable selection or curation\n",
      "Number of items: 0\n",
      "Title: mortality prediction in intensive care units with the super icu learner algorithm (sicula): a population-based study\n",
      "Number of items: 0\n",
      "Title: prevalence and nature of financial considerations documented in narrative clinical records in intensive care units\n",
      "Number of items: 0\n",
      "Title: development and validation of a prognostic nomogram for myocardial infarction patients in intensive care units: a retrospective cohort study\n",
      "Number of items: 0\n",
      "Title: relationship between \"1-hour serum lactate\" level and 30-day mortality in critical care patients in intensive care unit\n",
      "Number of items: 0\n",
      "Title: mortality prediction in intensive care units (icus) using a deep rule-based fuzzy classifier\n",
      "Number of items: 0\n",
      "Title: diuretic strategies in patients with resistance to loop-diuretics in the intensive care unit: a retrospective study from the mimic-iii database\n",
      "Number of items: 0\n",
      "Title: five novel clinical phenotypes for critically ill patients with mechanical ventilation in intensive care units: a retrospective and multi database study\n",
      "Number of items: 0\n",
      "Title: association between body mass index and short-term mortality in patients with intra-abdominal infections: a retrospective, single-centre cohort study using the medical information mart for intensive care database\n",
      "Number of items: 0\n",
      "Title: mortality prediction in cerebral hemorrhage patients using machine learning algorithms in intensive care units\n",
      "Number of items: 0\n",
      "Title: prognostic value of red blood cell distribution width in acute pancreatitis patients admitted to intensive care units: an analysis of a publicly accessible clinical database mimic ii\n",
      "Number of items: 0\n",
      "Title: a novel artificial intelligence based intensive care unit monitoring system: using physiological waveforms to identify sepsis\n",
      "Number of items: 0\n",
      "Title: the weekend effect for stroke patients admitted to intensive care: a retrospective cohort analysis\n",
      "Number of items: 0\n",
      "Title: critical transitions in intensive care units: a sepsis case study\n",
      "Number of items: 0\n",
      "Title: analysis of diseases distribution in medical information mart for intensive care iii database\n",
      "Number of items: 0\n",
      "Title: value of early critical care transthoracic echocardiography for patients undergoing mechanical ventilation: a retrospective study\n",
      "Number of items: 0\n",
      "Title: association between comorbid chronic obstructive pulmonary disease and prognosis of patients admitted to the intensive care unit for non-copd reasons: a retrospective cohort study\n",
      "Number of items: 0\n",
      "Title: monocyte/lymphocyte ratio as a predictor of 30-day mortality and adverse events in critically ill patients: analysis of the mimic-iii database\n",
      "Number of items: 0\n",
      "Title: cardio-pulmonary-renal interactions in icu patients. role of mechanical ventilation, venous congestion and perfusion deficit on worsening of renal function: insights from the mimic-iii database\n",
      "Number of items: 0\n",
      "Title: development and internal validation of a nomogram to predict mortality during the icu stay of thoracic fracture patients without neurological compromise: an analysis of the mimic-iii clinical database\n",
      "Number of items: 0\n",
      "Title: novel data imputation for multiple types of missing data in intensive care units\n",
      "Number of items: 0\n",
      "Title: improved survival of cancer patients admitted to the intensive care unit between 2002 and 2011 at a u.s. teaching hospital\n",
      "Number of items: 0\n",
      "Title: association of prescription opioid use on mortality and hospital length of stay in the intensive care unit\n",
      "Number of items: 0\n",
      "Title: prolonged elevated heart rate and 90-day survival in acutely ill patients: data from the mimic-iii database\n",
      "Number of items: 0\n",
      "Title: the impact of statin use prior to intensive care unit admission on critically ill patients with sepsis\n",
      "Number of items: 0\n",
      "Title: analysis of adult disease characteristics and mortality on mimic-iii\n",
      "Number of items: 0\n",
      "Title: a nomogram to predict in-hospital mortality for post-gastrointestinal resection surgery patients in intensive care units: a retrospective cohort study\n",
      "Number of items: 0\n",
      "Title: elevated lactate dehydrogenase levels display a poor prognostic factor for non-hodgkin's lymphoma in intensive care unit: an analysis of the mimic-iii database combined with external validation\n",
      "Number of items: 0\n",
      "Title: nocturnal mean arterial pressure rising is associated with mortality in the intensive care unit: a retrospective cohort study\n",
      "Number of items: 0\n",
      "Title: predicting lab values for gastrointestinal bleeding patients in the intensive care unit: a comparative study on the impact of comorbidities and medications\n",
      "Number of items: 0\n",
      "Title: performance of intensive care unit severity scoring systems across different ethnicities in the usa: a retrospective observational study\n",
      "Number of items: 0\n",
      "Title: the effect of ventilator-associated pneumonia on the prognosis of intensive care unit patients within 90 days and 180 days\n",
      "Number of items: 0\n",
      "Title: prediction of hypotension events with physiologic vital sign signatures in the intensive care unit\n",
      "Number of items: 0\n",
      "Title: impact and predictive value of time-dependent acute physiology and chronic health evaluation ii score on death risk in patients with severe stroke: data analysis based on mimic-iii\n",
      "Number of items: 0\n",
      "Title: mimic-iii, a freely accessible critical care database\n",
      "Number of items: 0\n",
      "Title: iseeu: visually interpretable deep learning for mortality prediction inside the icu\n",
      "Number of items: 0\n",
      "Title: using explainable machine learning to improve intensive care unit alarm systems\n",
      "Number of items: 0\n",
      "Title: the association between four scoring systems and 30-day mortality among intensive care patients with sepsis: a cohort study\n",
      "Number of items: 0\n",
      "Title: early hospital mortality prediction of intensive care unit patients using an ensemble learning approach\n",
      "Number of items: 0\n",
      "Title: atrial fibrillation detection during sepsis: study on mimic iii icu data\n",
      "Number of items: 0\n",
      "Title: a novel model to label delirium in an intensive care unit from clinician actions\n",
      "Number of items: 0\n",
      "Title: a machine learning sepsis prediction algorithm for intended intensive care unit use (navoy sepsis): proof-of-concept study\n",
      "Number of items: 0\n",
      "Title: central venous pressure measurement is associated with improved outcomes in septic patients: an analysis of the mimic-iii database\n",
      "Number of items: 0\n",
      "Title: a long short-term memory ensemble approach for improving the outcome prediction in intensive care unit\n",
      "Number of items: 0\n",
      "Title: machine learning outperforms existing clinical scoring tools in the prediction of postoperative atrial fibrillation during intensive care unit admission after cardiac surgery\n",
      "Number of items: 0\n",
      "Title: novel method of atrial fibrillation case identification and burden estimation using the mimic-iii electronic health data set\n",
      "Number of items: 0\n",
      "Title: sentiment analysis based on the nursing notes on in-hospital 28-day mortality of sepsis patients utilizing the mimic-iii database\n",
      "Number of items: 0\n",
      "Title: use of electronic health record data to evaluate the impact of race on 30-day mortality in patients admitted to the intensive care unit\n",
      "Number of items: 0\n",
      "Title: risk factors for pulmonary embolism in icu patients: a retrospective cohort study from the mimic-iii database\n",
      "Number of items: 0\n",
      "Title: introduction of critical care database based on specialized information systems: a model of critical care medicine database in large level iii grade a hospital\n",
      "Number of items: 0\n",
      "Title: an empirical evaluation of deep learning for icd-9 code assignment using mimic-iii clinical notes\n",
      "Number of items: 0\n",
      "Title: combination of static and temporal data analysis to predict mortality and readmission in the intensive care\n",
      "Number of items: 0\n",
      "Title: the obesity paradox in icu patients\n",
      "Number of items: 0\n",
      "Title: nomogram to predict the risk of acute kidney injury in patients with diabetic ketoacidosis: an analysis of the mimic-iii database\n",
      "Number of items: 0\n",
      "Title: early prediction of acute kidney injury following icu admission using a multivariate panel of physiological measurements\n",
      "Number of items: 0\n",
      "Title: effect of early hyperoxemia on mortality in mechanically ventilated septic shock patients according to sepsis-3 criteria: analysis of the mimic-iii database\n",
      "Number of items: 0\n",
      "Title: a nomogram to predict in-hospital mortality of neonates admitted to the intensive care unit\n",
      "Number of items: 0\n",
      "Title: association of sex with clinical outcome in critically ill sepsis patients: a retrospective analysis of the large clinical database mimic-iii\n",
      "Number of items: 0\n",
      "Title: prediction of early unplanned intensive care unit readmission in a uk tertiary care hospital: a cross-sectional machine learning approach\n",
      "Number of items: 0\n",
      "Title: application of support vector machine in predicting in-hospital mortality risk of patients with acute kidney injury in icu\n",
      "Number of items: 0\n",
      "Title: nomogram to predict the risk of septic acute kidney injury in the first 24 h of admission: an analysis of intensive care unit data\n",
      "Number of items: 0\n",
      "Title: identifying risk factors for pressure injury in adult critical care patients\n",
      "Number of items: 0\n",
      "Title: lower serum chloride concentrations are associated with increased risk of mortality in critically ill cirrhotic patients: an analysis of the mimic-iii database\n",
      "Number of items: 0\n",
      "Title: multiparameter intelligent monitoring in intensive care ii: a public-access intensive care unit database\n",
      "Number of items: 0\n",
      "Title: analysis and prediction of unplanned intensive care unit readmission using recurrent neural networks with long short-term memory\n",
      "Number of items: 0\n",
      "Title: using machine learning methods to predict in-hospital mortality of sepsis patients in the icu\n",
      "Number of items: 0\n",
      "Title: an explainable machine learning algorithm for risk factor analysis of in-hospital mortality in sepsis survivors with icu readmission\n",
      "Number of items: 0\n",
      "Title: machine learning for the prediction of volume responsiveness in patients with oliguric acute kidney injury in critical care\n",
      "Number of items: 0\n",
      "Title: lactate indices as predictors of in-hospital mortality or 90-day survival after admission to an intensive care unit in unselected critically ill patients\n",
      "Number of items: 0\n",
      "Title: performance of d-dimer for predicting sepsis mortality in the intensive care unit\n",
      "Number of items: 0\n",
      "Title: a novel switching state-space model for post-icu mortality prediction and survival analysis\n",
      "Number of items: 0\n",
      "Title: data-driven curation process for describing the blood glucose management in the intensive care unit\n",
      "Number of items: 0\n",
      "Title: neural network predicts need for red blood cell transfusion for patients with acute gastrointestinal bleeding admitted to the intensive care unit\n",
      "Number of items: 0\n",
      "Title: examination of electrolyte replacements in the icu utilizing mimic-iii dataset demonstrates redundant replacement patterns\n",
      "Number of items: 0\n",
      "Title: the critical care data exchange format: a proposed flexible data standard for combining clinical and high-frequency physiologic data in critical care\n",
      "Number of items: 0\n",
      "Title: an interpretable intensive care unit mortality risk calculator\n",
      "Number of items: 0\n",
      "Title: prognostic nomogram for acute pancreatitis patients: an analysis of publicly electronic healthcare records in intensive care unit\n",
      "Number of items: 0\n",
      "Title: dexmedetomidine versus midazolam and propofol for sedation in critically ill patients: mining the medical information mart for intensive care data\n",
      "Number of items: 0\n",
      "Title: sentiment in nursing notes as an indicator of out-of-hospital mortality in intensive care patients\n",
      "Number of items: 0\n",
      "Title: incorporating real-world evidence into the development of patient blood glucose prediction algorithms for the icu\n",
      "Number of items: 0\n",
      "Title: serum anion gap at admission predicts all-cause mortality in critically ill patients with cerebral infarction: evidence from the mimic-iii database\n",
      "Number of items: 0\n",
      "Title: the association between neutrophil-to-lymphocyte count ratio and mortality in septic patients: a retrospective analysis of the mimic-iii database\n",
      "Number of items: 0\n",
      "Title: serum anion gap predicts all-cause mortality in critically ill patients with acute kidney injury: analysis of the mimic-iii database\n",
      "Number of items: 0\n",
      "Title: accessing the public mimic-ii intensive care relational database for clinical research\n",
      "Number of items: 0\n",
      "Title: heuristics to determine ventilation times of icu patients from the mimic-ii database\n",
      "Number of items: 0\n",
      "Title: red blood cell distribution width predicts long-term outcomes in sepsis patients admitted to the intensive care unit\n",
      "Number of items: 0\n",
      "Title: multitask prediction of organ dysfunction in the intensive care unit using sequential subnetwork routing\n",
      "Number of items: 0\n",
      "Title: withholding or withdrawing invasive interventions may not accelerate time to death among dying icu patients\n",
      "Number of items: 0\n",
      "Title: reducing false asystole alarms in intensive care\n",
      "Number of items: 0\n",
      "Title: a machine learning-based model for 1-year mortality prediction in patients admitted to an intensive care unit with a diagnosis of sepsis\n",
      "Number of items: 0\n",
      "Title: health status monitoring for icu patients based on locally weighted principal component analysis\n",
      "Number of items: 0\n",
      "Title: predicting risk for trauma patients using static and dynamic information from the mimic iii database\n",
      "Number of items: 0\n",
      "Title: leveraging a critical care database: selective serotonin reuptake inhibitor use prior to icu admission is associated with increased hospital mortality\n",
      "Number of items: 0\n",
      "Title: adaptive online monitoring for icu patients by combining just-in-time learning and principal component analysis\n",
      "Number of items: 0\n",
      "Title: development of acute kidney injury prognostic model for critically ill patients based on mimic-iii database\n",
      "Number of items: 0\n",
      "Title: machine learning prediction models for mechanically ventilated patients: analyses of the mimic-iii database\n",
      "Number of items: 0\n",
      "Title: predicting icu readmission using grouped physiological and medication trends\n",
      "Number of items: 0\n",
      "Title: machine learning for real-time prediction of complications in critical care: a retrospective study\n",
      "Number of items: 0\n",
      "Title: clinical data mining by exploring public mimic-ii intensive care database\n",
      "Number of items: 0\n",
      "Title: effects of etomidate use in icu patients on ventilator therapy: a study of 12,526 patients in an open database from a single center\n",
      "Number of items: 0\n",
      "Title: long-term mortality predictors of icu fungaemia\n",
      "Number of items: 0\n",
      "Title: fluid-limiting treatment strategies among sepsis patients in the icu: a retrospective causal analysis\n",
      "Number of items: 0\n",
      "Title: red blood cell distribution width and neutrophil to lymphocyte ratio are associated with outcomes of adult subarachnoid haemorrhage patients admitted to intensive care unit\n",
      "Number of items: 0\n",
      "Title: early prediction of acute kidney injury in critical care setting using clinical notes and structured multivariate physiological measurements\n",
      "Number of items: 0\n",
      "Title: big data and machine learning in critical care: opportunities for collaborative research\n",
      "Number of items: 0\n",
      "Title: an attention based deep learning model of clinical events in the intensive care unit\n",
      "Number of items: 0\n",
      "Title: arterial oxygen pressure targets in critically ill patients: analysis of a large icu database\n",
      "Number of items: 0\n",
      "Title: towards a decision support tool for intensive care discharge: machine learning algorithm development using electronic healthcare data from mimic-iii and bristol, uk\n",
      "Number of items: 0\n",
      "Title: the relationship between the serum anion gap and all-cause mortality in acute pancreatitis: an analysis of the mimic-iii database\n",
      "Number of items: 0\n",
      "Title: development of a nomogram to predict 28-day mortality of patients with sepsis-induced coagulopathy: an analysis of the mimic-iii database\n",
      "Number of items: 0\n",
      "Title: predicting open wound mortality in the icu using machine learning\n",
      "Number of items: 0\n",
      "Title: n-gram support vector machines for scalable procedure and diagnosis classification, with applications to clinical free text data from the intensive care unit\n",
      "Number of items: 0\n",
      "Title: predictive value of ionized calcium in critically ill patients: an analysis of a large clinical database mimic ii\n",
      "Number of items: 0\n",
      "Title: a clinically practical and interpretable deep model for icu mortality prediction with external validation\n",
      "Number of items: 0\n",
      "Title: predicting the incidence of pressure ulcers in the intensive care unit using machine learning\n",
      "Number of items: 0\n",
      "Title: interpretability of time-series deep learning models: a study in cardiovascular patients admitted to intensive care unit\n",
      "Number of items: 0\n",
      "Title: septic shock prediction for icu patients via coupled hmm walking on sequential contrast patterns\n",
      "Number of items: 0\n",
      "Title: predicting hyperlactatemia in the mimic ii database\n",
      "Number of items: 0\n",
      "Title: predicting in-hospital mortality of patients with acute kidney injury in the icu using random forest model\n",
      "Number of items: 0\n",
      "Title: using demographic and time series physiological features to classify sepsis in the intensive care unit\n",
      "Number of items: 0\n",
      "Title: the incidence and outcome of severe hyperlactatemia in critically ill patients\n",
      "Number of items: 0\n",
      "Title: predicting severe clinical events by learning about life-saving actions and outcomes using distant supervision\n",
      "Number of items: 0\n",
      "Title: a deep learning backcasting approach to the electrolyte, metabolite, and acid-base parameters that predict risk in icu patients\n",
      "Number of items: 0\n",
      "Title: association between early central venous pressure measurement and mortality in patients with sepsis: a data analysis of mimic-iii database\n",
      "Number of items: 0\n",
      "Title: using information theory to identify redundancy in common laboratory tests in the intensive care unit\n",
      "Number of items: 0\n",
      "Title: comparison of four scoring systems for predicting icu mortality in patients with sepsis\n",
      "Number of items: 0\n",
      "Title: lower short- and long-term mortality associated with overweight and obesity in a large cohort study of adult intensive care unit patients\n",
      "Number of items: 0\n",
      "Title: umls mapping and word embeddings for icd code assignment using the mimic-iii intensive care database\n",
      "Number of items: 0\n",
      "Title: predicting hypotension in the icu using noninvasive physiological signals\n",
      "Number of items: 0\n",
      "Title: open-access mimic-ii database for intensive care research\n",
      "Number of items: 0\n",
      "Title: a web-based data visualization tool for the mimic-ii database\n",
      "Number of items: 0\n",
      "Title: hypotension risk prediction via sequential contrast patterns of icu blood pressure\n",
      "Number of items: 0\n",
      "Title: customization of a severity of illness score using local electronic medical record data\n",
      "Number of items: 0\n",
      "Title: fuzzy modeling to predict severely depressed left ventricular ejection fraction following admission to the intensive care unit using clinical physiology\n",
      "Number of items: 0\n",
      "Title: do the types and routes of proton pump inhibitor treatments affect <i>clostridium difficile</i> in icu patients? a retrospective cohort study\n",
      "Number of items: 0\n",
      "Title: a new prediction model for assessing the clinical outcomes of icu patients with community-acquired pneumonia: a decision tree analysis\n",
      "Number of items: 0\n",
      "Title: robust monitoring of hypovolemia in intensive care patients using photoplethysmogram signals\n",
      "Number of items: 0\n",
      "Title: unpacking prevalence and dichotomy in quick sequential organ failure assessment and systemic inflammatory response syndrome parameters: observational data-driven approach backed by sepsis pathophysiology\n",
      "Number of items: 0\n",
      "Title: machine learning predicts mortality in septic patients using only routinely available abg variables: a multi-centre evaluation\n",
      "Number of items: 0\n",
      "Title: association between obstructive sleep apnea and reduced mortality in critically ill patients: a propensity score-based analysis\n",
      "Number of items: 0\n",
      "Title: inhaled beta2-agonists increase in-hospital mortality in icu patients with heart failure\n",
      "Number of items: 0\n",
      "Title: mortality prediction for patients with acute respiratory distress syndrome based on machine learning: a population-based study\n",
      "Number of items: 0\n",
      "Title: prediction of an acute hypotensive episode during an icu hospitalization with a super learner machine-learning algorithm\n",
      "Number of items: 0\n",
      "Title: predictive modeling of bacterial infections and antibiotic therapy needs in critically ill adults\n",
      "Number of items: 0\n",
      "Title: benchmarking deep learning architectures for predicting readmission to the icu and describing patients-at-risk\n",
      "Number of items: 0\n",
      "Title: developing and verifying a multivariate model to predict the survival probability after coronary artery bypass grafting in patients with coronary atherosclerosis based on the mimic-iii database\n",
      "Number of items: 0\n",
      "Title: a retrospective cohort study on the association between red cell distribution width and all-cause mortality of patients with cholecystitis at icu admission\n",
      "Number of items: 0\n",
      "Title: improvement of apache ii score system for disease severity based on xgboost algorithm\n",
      "Number of items: 0\n",
      "Title: atrial fibrillation detection in icu patients: a pilot study on mimic iii data<sup>.</sup>\n",
      "Number of items: 0\n",
      "Title: m\n",
      "Number of items: 0\n",
      "Title: development and validation of a novel blending machine learning model for hospital mortality prediction in icu patients with sepsis\n",
      "Number of items: 0\n",
      "Title: prediction of septic shock onset in icu by instantaneous monitoring of vital signs<sup>.</sup>\n",
      "Number of items: 0\n",
      "Title: effect of ondansetron on reducing icu mortality in patients with acute kidney injury\n",
      "Number of items: 0\n",
      "Title: predicting prolonged length of icu stay through machine learning\n",
      "Number of items: 0\n",
      "Title: democratizing ehr analyses with fiddle: a flexible data-driven preprocessing pipeline for structured clinical data\n",
      "Number of items: 0\n",
      "Title: association of thiamine use with outcomes in patients with sepsis and alcohol use disorder: an analysis of the mimic-iii database\n",
      "Number of items: 0\n",
      "Title: admission hyperglycemia predicts long-term mortality in critically ill patients with subarachnoid hemorrhage: a retrospective analysis of the mimic-iii database\n",
      "Number of items: 0\n",
      "Title: mean arterial pressure and mortality in patients with distributive shock: a retrospective analysis of the mimic-iii database\n",
      "Number of items: 0\n",
      "Title: understanding the complexity of sepsis mortality prediction via rule discovery and analysis: a pilot study\n",
      "Number of items: 0\n",
      "Title: efficient and sparse feature selection for biomedical text classification via the elastic net: application to icu risk stratification from nursing notes\n",
      "Number of items: 0\n",
      "Title: admission white blood cell count predicts post-discharge mortality in patients with acute aortic dissection: data from the mimic-iii database\n",
      "Number of items: 0\n",
      "Title: impact of sex on use of low tidal volume ventilation in invasively ventilated icu patients-a mediation analysis using two observational cohorts\n",
      "Number of items: 0\n",
      "Title: overweight is associated with better one-year survival in elderly patients after cardiac surgery: a retrospective analysis of the mimic-iii database\n",
      "Number of items: 0\n",
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      "Number of items: 0\n",
      "Title: predictive value of lactate in unselected critically ill patients: an analysis using fractional polynomials\n",
      "Number of items: 0\n",
      "Title: association between red blood cell distribution width-to-albumin ratio and prognosis of patients with aortic aneurysms\n",
      "Number of items: 0\n",
      "Title: mechanical power normalized to predicted body weight is associated with mortality in critically ill patients: a cohort study\n",
      "Number of items: 0\n",
      "Title: blood urea nitrogen to serum albumin ratio as a new prognostic indicator in critical patients with chronic heart failure\n",
      "Number of items: 0\n",
      "Title: association between anion gap and mortality in critically ill patients with cardiogenic shock\n",
      "Number of items: 0\n",
      "Title: an explainable cnn approach for medical codes prediction from clinical text\n",
      "Number of items: 0\n",
      "Title: lower mean corpuscular hemoglobin concentration is associated with poorer outcomes in intensive care unit admitted patients with acute myocardial infarction\n",
      "Number of items: 0\n",
      "Title: comparative efficacy of fentanyl and morphine in patients with or at risk for acute respiratory distress syndrome: a propensity score-matched cohort study\n",
      "Number of items: 0\n",
      "Title: a new scoring system for predicting in-hospital death in patients having liver cirrhosis with esophageal varices\n",
      "Number of items: 0\n",
      "Title: atrial fibrillation prediction from critically ill sepsis patients\n",
      "Number of items: 0\n",
      "Title: effectiveness of sodium bicarbonate infusion on mortality in septic patients with metabolic acidosis\n",
      "Number of items: 0\n",
      "Title: the association between the baseline and the change in neutrophil-to-lymphocyte ratio and short-term mortality in patients with acute respiratory distress syndrome\n",
      "Number of items: 0\n",
      "Title: development and evaluation of predictive alerts for hemodynamic instability in icu patients\n",
      "Number of items: 0\n",
      "Title: an early respiratory distress detection method with markov models\n",
      "Number of items: 0\n",
      "Title: classifying social determinants of health from unstructured electronic health records using deep learning-based natural language processing\n",
      "Number of items: 0\n",
      "Title: development and validation of an automated algorithm to detect atrial fibrillation within stored intensive care unit continuous electrocardiographic data: observational study\n",
      "Number of items: 0\n",
      "Title: preoperative liver function test abnormalities were associated with short-term and long-term prognosis in cardiac surgery patients without liver disease\n",
      "Number of items: 0\n",
      "Title: increased neutrophil percentage-to-albumin ratio is associated with all-cause mortality in patients with severe sepsis or septic shock\n",
      "Number of items: 0\n",
      "Title: robust algorithm to locate heart beats from multiple physiological waveforms by individual signal detector voting\n",
      "Number of items: 0\n",
      "Title: an adaptive weight learning-based multitask deep network for continuous blood pressure estimation using electrocardiogram signals\n",
      "Number of items: 0\n",
      "Title: relationship between early serum sodium and potassium levels and aki severity and prognosis in oliguric aki patients\n",
      "Number of items: 0\n",
      "Title: deceased serum bilirubin and albumin levels in the assessment of severity and mortality in patients with acute pancreatitis\n",
      "Number of items: 0\n",
      "Title: automatic medical code assignment via deep learning approach for intelligent healthcare\n",
      "Number of items: 0\n",
      "Title: novel density poincaré plot based machine learning method to detect atrial fibrillation from premature atrial/ventricular contractions\n",
      "Number of items: 0\n",
      "Title: extraction of information related to drug safety surveillance from electronic health record notes: joint modeling of entities and relations using knowledge-aware neural attentive models\n",
      "Number of items: 0\n",
      "Title: automated icd coding via unsupervised knowledge integration (unite)\n",
      "Number of items: 0\n",
      "Title: antipyretic therapy in critically ill patients with sepsis: an interaction with body temperature\n",
      "Number of items: 0\n",
      "Title: comparative effectiveness of midazolam, propofol, and dexmedetomidine in patients with or at risk for acute respiratory distress syndrome: a propensity score-matched cohort study\n",
      "Number of items: 0\n",
      "Title: construction of multi-parameter emergency database and preliminary application research\n",
      "Number of items: 0\n",
      "Title: prognostic role of ammonia in critical care patients without known hepatic disease\n",
      "Number of items: 0\n",
      "Title: medextractr: a targeted, customizable approach to medication extraction from electronic health records\n",
      "Number of items: 0\n",
      "Title: using machine learning to predict hyperchloremia in critically ill patients\n",
      "Number of items: 0\n",
      "Title: comparison of approaches for prediction of renal replacement therapy-free survival in patients with acute kidney injury\n",
      "Number of items: 0\n",
      "Title: increased body mass index linked to greater short- and long-term survival in sepsis patients: a retrospective analysis of a large clinical database\n",
      "Number of items: 0\n",
      "Title: a methodology for prediction of acute hypotensive episodes in icu via a risk scoring model including analysis of st-segment variations\n",
      "Number of items: 0\n",
      "Title: on-device reliability assessment and prediction of missing photoplethysmographic data using deep neural networks\n",
      "Number of items: 0\n",
      "Title: relationship between 24-h venous blood glucose variation and mortality among patients with acute respiratory failure\n",
      "Number of items: 0\n",
      "Title: a machine learning-based prediction of hospital mortality in patients with postoperative sepsis\n",
      "Number of items: 0\n",
      "Title: the neutrophil-lymphocyte ratio is associated with postoperative mortality of cardiac surgery\n",
      "Number of items: 0\n",
      "Title: clinical value of the lactate/albumin ratio and lactate/albumin ratio × age score in the assessment of prognosis in patients with sepsis\n",
      "Number of items: 0\n",
      "Title: empirical findings on the role of structured data, unstructured data, and their combination for automatic clinical phenotyping\n",
      "Number of items: 0\n",
      "Title: red blood cell distribution width as a predictor of 28-day mortality in critically ill patients with alcohol use disorder\n",
      "Number of items: 0\n",
      "Title: recurrent neural network models for blood pressure monitoring using ppg morphological features\n",
      "Number of items: 0\n",
      "Title: neutrophil-lymphocyte ratio is associated with all-cause mortality among critically ill patients with acute kidney injury\n",
      "Number of items: 0\n",
      "Title: risk potential for organ dysfunction associated with sodium bicarbonate therapy in critically ill patients with hemodynamic worsening\n",
      "Number of items: 0\n",
      "Title: associations between elevated systolic blood pressure and outcomes in critically ill patients: a retrospective cohort study and propensity analysis\n",
      "Number of items: 0\n",
      "Title: blood pressure estimation based on pulse arrival time and heart rate : a correlation analysis for critically ill patients<sup>.</sup>\n",
      "Number of items: 0\n",
      "Title: uncertainty-gated stochastic sequential model for ehr mortality prediction\n",
      "Number of items: 0\n",
      "Title: associations between early thiamine administration and clinical outcomes in critically ill patients with acute kidney injury\n",
      "Number of items: 0\n",
      "Title: the red blood cell distribution width-albumin ratio was a potential prognostic biomarker for diabetic ketoacidosis\n",
      "Number of items: 0\n",
      "Title: the red blood cell distribution width-albumin ratio: a promising predictor of mortality in stroke patients\n",
      "Number of items: 0\n",
      "Title: uncertainty-aware variational-recurrent imputation network for clinical time series\n",
      "Number of items: 0\n",
      "Title: relationship between first 24-h mean body temperature and clinical outcomes of post-cardiac surgery patients\n",
      "Number of items: 0\n",
      "Title: a new analytical framework for missing data imputation and classification with uncertainty: missing data imputation and heart failure readmission prediction\n",
      "Number of items: 0\n",
      "Title: discriminative and distinct phenotyping by constrained tensor factorization\n",
      "Number of items: 0\n",
      "Title: early biliary drainage is associated with favourable outcomes in critically-ill patients with acute cholangitis\n",
      "Number of items: 0\n",
      "Title: a data mining approach to reduce the false alarm rate of patient monitors\n",
      "Number of items: 0\n",
      "Title: fluctuations of driving pressure during mechanical ventilation indicates elevated central venous pressure and poor outcomes\n",
      "Number of items: 0\n",
      "Title: customization scenarios for de-identification of clinical notes\n",
      "Number of items: 0\n",
      "Title: effect of antipyretic therapy on mortality in critically ill patients with sepsis receiving mechanical ventilation treatment\n",
      "Number of items: 0\n",
      "Title: leukocytes in critical patients with asthma exacerbation\n",
      "Number of items: 0\n",
      "Title: development of a novel score for the prediction of hospital mortality in patients with severe sepsis: the use of electronic healthcare records with lasso regression\n",
      "Number of items: 0\n",
      "Title: hypoxic hepatitis - its biochemical profile, causes and risk factors of mortality in critically-ill patients: a cohort study of 565 patients\n",
      "Number of items: 0\n",
      "Title: time-related association between fluid balance and mortality in sepsis patients: interaction between fluid balance and haemodynamics\n",
      "Number of items: 0\n",
      "Title: association of post-operative systolic blood pressure variability with mortality after coronary artery bypass grafting\n",
      "Number of items: 0\n",
      "Title: impact of diagnosis code grouping method on clinical prediction model performance: a multi-site retrospective observational study\n",
      "Number of items: 0\n",
      "Title: acidosis predicts mortality independently from hyperlactatemia in patients with sepsis\n",
      "Number of items: 0\n",
      "Title: a hypotensive episode predictor for intensive care based on heart rate and blood pressure time series\n",
      "Number of items: 0\n",
      "Title: high serum iron level is associated with increased mortality in patients with sepsis\n",
      "Number of items: 0\n",
      "Title: reducing false alarm rates for critical arrhythmias using the arterial blood pressure waveform\n",
      "Number of items: 0\n",
      "Title: collective experience: a database-fuelled, inter-disciplinary team-led learning system\n",
      "Number of items: 0\n",
      "Title: recurrent neural networks for multivariate time series with missing values\n",
      "Number of items: 0\n",
      "Title: an explainable machine learning framework for lung cancer hospital length of stay prediction\n",
      "Number of items: 0\n",
      "Title: heart rate fluctuation and mortality in critically ill myocardial infarction patients: a retrospective cohort study\n",
      "Number of items: 0\n",
      "Title: construction and evaluation of a sepsis risk prediction model for urinary tract infection\n",
      "Number of items: 0\n",
      "Title: hypotension as a risk factor for acute kidney injury in icu patients\n",
      "Number of items: 0\n",
      "Title: predictive value of red blood cell distribution width in critically ill patients with atrial fibrillation: a retrospective cohort study\n",
      "Number of items: 0\n",
      "Title: prognostic association between perioperative red blood cell transfusion and postoperative cardiac surgery outcomes\n",
      "Number of items: 0\n",
      "Title: association of systemic immune-inflammation index with short-term mortality of congestive heart failure: a retrospective cohort study\n",
      "Number of items: 0\n",
      "Title: systemic immune-inflammation index for predicting the prognosis of critically ill patients with acute pancreatitis\n",
      "Number of items: 0\n",
      "Title: association of fib-4 index and clinical outcomes in critically ill patients with acute kidney injury: a cohort study\n",
      "Number of items: 0\n",
      "Title: a boosting inspired personalized threshold method for sepsis screening\n",
      "Number of items: 0\n",
      "Title: systemic immune-inflammation index as a promising predictor of mortality in patients with acute coronary syndrome: a real-world study\n",
      "Number of items: 0\n",
      "Title: serum anion gap is associated with risk of all-cause mortality in critically ill patients with acute myocardial infarction\n",
      "Number of items: 0\n",
      "Title: hierarchical information criterion for variable abstraction\n",
      "Number of items: 0\n",
      "Title: robust ppg motion artifact detection using a 1-d convolution neural network\n",
      "Number of items: 0\n",
      "Title: dr. agent: clinical predictive model via mimicked second opinions\n",
      "Number of items: 0\n",
      "Title: variable selection for causal mediation analysis using lasso-based methods\n",
      "Number of items: 0\n",
      "Title: role of empiric antifungal therapy in patients with perforated peptic ulcers\n",
      "Number of items: 0\n",
      "Title: reducing false arrhythmia alarm rates using robust heart rate estimation and cost-sensitive support vector machines\n",
      "Number of items: 0\n",
      "Title: development and validation of a nomogram to predict the mortality risk in elderly patients with arf\n",
      "Number of items: 0\n",
      "Title: association between albumin infusion and outcomes in patients with acute kidney injury and septic shock\n",
      "Number of items: 0\n",
      "Title: predictive value of red blood cell distribution width in septic shock patients with thrombocytopenia: a retrospective study using machine learning\n",
      "Number of items: 0\n",
      "Title: machine learning prediction models for prognosis of critically ill patients after open-heart surgery\n",
      "Number of items: 0\n",
      "Title: development and validation of a simple-to-use nomogram for predicting in-hospital mortality in patients with acute heart failure undergoing continuous renal replacement therapy\n",
      "Number of items: 0\n",
      "Title: serum total bilirubin level is associated with hospital mortality rate in adult critically ill patients: a retrospective study\n",
      "Number of items: 0\n",
      "Title: positive association between systemic immune-inflammatory index and mortality of cardiogenic shock\n",
      "Number of items: 0\n",
      "Title: x-cal: explicit calibration for survival analysis\n",
      "Number of items: 0\n",
      "Title: the hyperbilirubinemia and potential predictors influence on long-term outcomes in sepsis: a population-based propensity score-matched study\n",
      "Number of items: 0\n",
      "Title: red blood cell distribution width is associated with all-cause mortality in critically ill patients with cardiogenic shock\n",
      "Number of items: 0\n",
      "Title: taper: time-aware patient ehr representation\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "object of type 'NoneType' has no len()",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[0;32mIn [10]\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m search_url \u001b[38;5;241m=\u001b[39m base_url \u001b[38;5;241m+\u001b[39m urllib\u001b[38;5;241m.\u001b[39mparse\u001b[38;5;241m.\u001b[39mquote_plus(title)\n\u001b[1;32m      7\u001b[0m content \u001b[38;5;241m=\u001b[39m simple_get(search_url)\n\u001b[0;32m----> 8\u001b[0m html \u001b[38;5;241m=\u001b[39m \u001b[43mBeautifulSoup\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcontent\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mhtml.parser\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      9\u001b[0m \u001b[38;5;66;03m# The publication type. May be present.\u001b[39;00m\n\u001b[1;32m     10\u001b[0m pub_item \u001b[38;5;241m=\u001b[39m html\u001b[38;5;241m.\u001b[39mfind(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124ma\u001b[39m\u001b[38;5;124m'\u001b[39m, alsec\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mptyp\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "File \u001b[0;32m~/Documents/citation-tracker/env/lib/python3.8/site-packages/bs4/__init__.py:312\u001b[0m, in \u001b[0;36mBeautifulSoup.__init__\u001b[0;34m(self, markup, features, builder, parse_only, from_encoding, exclude_encodings, element_classes, **kwargs)\u001b[0m\n\u001b[1;32m    310\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(markup, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mread\u001b[39m\u001b[38;5;124m'\u001b[39m):        \u001b[38;5;66;03m# It's a file-type object.\u001b[39;00m\n\u001b[1;32m    311\u001b[0m     markup \u001b[38;5;241m=\u001b[39m markup\u001b[38;5;241m.\u001b[39mread()\n\u001b[0;32m--> 312\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mmarkup\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m256\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m (\n\u001b[1;32m    313\u001b[0m         (\u001b[38;5;28misinstance\u001b[39m(markup, \u001b[38;5;28mbytes\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;124mb\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m<\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01min\u001b[39;00m markup)\n\u001b[1;32m    314\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m (\u001b[38;5;28misinstance\u001b[39m(markup, \u001b[38;5;28mstr\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m<\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01min\u001b[39;00m markup)\n\u001b[1;32m    315\u001b[0m ):\n\u001b[1;32m    316\u001b[0m     \u001b[38;5;66;03m# Print out warnings for a couple beginner problems\u001b[39;00m\n\u001b[1;32m    317\u001b[0m     \u001b[38;5;66;03m# involving passing non-markup to Beautiful Soup.\u001b[39;00m\n\u001b[1;32m    318\u001b[0m     \u001b[38;5;66;03m# Beautiful Soup will still parse the input as markup,\u001b[39;00m\n\u001b[1;32m    319\u001b[0m     \u001b[38;5;66;03m# just in case that's what the user really wants.\u001b[39;00m\n\u001b[1;32m    320\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m (\u001b[38;5;28misinstance\u001b[39m(markup, \u001b[38;5;28mstr\u001b[39m)\n\u001b[1;32m    321\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39msupports_unicode_filenames):\n\u001b[1;32m    322\u001b[0m         possible_filename \u001b[38;5;241m=\u001b[39m markup\u001b[38;5;241m.\u001b[39mencode(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mutf8\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[0;31mTypeError\u001b[0m: object of type 'NoneType' has no len()"
     ]
    }
   ],
   "source": [
    "all_mesh_terms = []\n",
    "all_publication_types = []\n",
    "\n",
    "for title in titles:\n",
    "    print('Title: {}'.format(title))\n",
    "    search_url = base_url + urllib.parse.quote_plus(title)\n",
    "    content = simple_get(search_url)\n",
    "    html = BeautifulSoup(content, 'html.parser')\n",
    "    # The publication type. May be present.\n",
    "    pub_item = html.find('a', alsec='ptyp')\n",
    "    if pub_item:\n",
    "        all_publication_types.append(pub_item.text)\n",
    "    # The mesh items\n",
    "    mesh_items = html.find_all('a', alsec='mesh')\n",
    "    print('Number of items: {}'.format(len(mesh_items)))\n",
    "    for item in mesh_items:\n",
    "        all_mesh_terms.append(item.text)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the results\n",
    "cx.write_lines(os.path.join('search_results', 'pubmed', 'publication-types.txt'), all_publication_types)\n",
    "cx.write_lines(os.path.join('search_results', 'pubmed', 'mesh-terms.txt'), all_mesh_terms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Research Support, N.I.H., Extramural',\n",
       " 'Research Support, N.I.H., Intramural',\n",
       " 'Research Support, N.I.H., Extramural',\n",
       " 'Research Support, N.I.H., Extramural',\n",
       " 'Comparative Study',\n",
       " \"Research Support, Non-U.S. Gov't\",\n",
       " \"Research Support, Non-U.S. Gov't\",\n",
       " 'Research Support, N.I.H., Intramural',\n",
       " \"Research Support, Non-U.S. Gov't\",\n",
       " \"Research Support, Non-U.S. Gov't\",\n",
       " 'Research Support, N.I.H., Extramural',\n",
       " 'Research Support, N.I.H., Extramural',\n",
       " 'Research Support, N.I.H., Extramural',\n",
       " \"Research Support, Non-U.S. Gov't\",\n",
       " 'Research Support, N.I.H., Extramural',\n",
       " 'Research Support, N.I.H., Extramural',\n",
       " \"Research Support, Non-U.S. Gov't\",\n",
       " 'Research Support, N.I.H., Extramural',\n",
       " 'Observational Study',\n",
       " \"Research Support, Non-U.S. Gov't\",\n",
       " \"Research Support, Non-U.S. Gov't\",\n",
       " \"Research Support, Non-U.S. Gov't\",\n",
       " 'Research Support, N.I.H., Extramural',\n",
       " 'Comparative Study',\n",
       " 'Research Support, N.I.H., Extramural',\n",
       " \"Research Support, Non-U.S. Gov't\",\n",
       " 'Research Support, N.I.H., Extramural',\n",
       " \"Research Support, U.S. Gov't, Non-P.H.S.\",\n",
       " 'Research Support, N.I.H., Extramural',\n",
       " 'Research Support, N.I.H., Extramural',\n",
       " \"Research Support, Non-U.S. Gov't\",\n",
       " 'Evaluation Studies',\n",
       " \"Research Support, U.S. Gov't, Non-P.H.S.\",\n",
       " \"Research Support, Non-U.S. Gov't\",\n",
       " 'Research Support, N.I.H., Extramural',\n",
       " \"Research Support, Non-U.S. Gov't\",\n",
       " 'Research Support, N.I.H., Extramural',\n",
       " 'Comparative Study',\n",
       " \"Research Support, Non-U.S. Gov't\",\n",
       " 'Comparative Study',\n",
       " 'Comparative Study',\n",
       " \"Research Support, Non-U.S. Gov't\",\n",
       " 'Research Support, N.I.H., Extramural',\n",
       " 'Research Support, N.I.H., Intramural',\n",
       " 'Comparative Study',\n",
       " 'Research Support, N.I.H., Extramural',\n",
       " 'Research Support, N.I.H., Extramural']"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_publication_types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Aged',\n",
       " 'Aged, 80 and over',\n",
       " 'Algorithms',\n",
       " 'Boston',\n",
       " 'Female',\n",
       " 'Hospital Mortality*',\n",
       " 'Humans',\n",
       " 'Intensive Care Units/statistics & numerical data*',\n",
       " 'Machine Learning/statistics & numerical data*',\n",
       " 'Male',\n",
       " 'Middle Aged',\n",
       " 'ROC Curve',\n",
       " 'Reproducibility of Results',\n",
       " 'Risk',\n",
       " 'Severity of Illness Index*',\n",
       " 'Acute Disease',\n",
       " 'Aged',\n",
       " 'Databases, Factual*',\n",
       " 'Erythrocyte Indices*',\n",
       " 'Female',\n",
       " 'Humans',\n",
       " 'Intensive Care Units*',\n",
       " 'Male',\n",
       " 'Middle Aged',\n",
       " 'Multivariate Analysis',\n",
       " 'Pancreatitis/blood*',\n",
       " 'Pancreatitis/diagnosis*',\n",
       " 'Prognosis',\n",
       " 'Retrospective Studies',\n",
       " 'Academic Medical Centers',\n",
       " 'Adult',\n",
       " 'African Americans/statistics & numerical data',\n",
       " 'Aged',\n",
       " 'Aged, 80 and over',\n",
       " 'Asian Americans/statistics & numerical data',\n",
       " 'Boston/epidemiology',\n",
       " 'Continental Population Groups/statistics & numerical data*',\n",
       " 'Electronic Health Records',\n",
       " 'European Continental Ancestry Group/statistics & numerical data',\n",
       " 'Female',\n",
       " 'Health Status Disparities*',\n",
       " 'Hospital Mortality/ethnology*',\n",
       " 'Humans',\n",
       " 'Intensive Care Units*',\n",
       " 'Male',\n",
       " 'Middle Aged',\n",
       " 'Retrospective Studies',\n",
       " 'Adult',\n",
       " 'Artificial Intelligence',\n",
       " 'Critical Care/statistics & numerical data*',\n",
       " 'Databases, Factual*',\n",
       " 'Decision Support Systems, Clinical*',\n",
       " 'Expert Systems',\n",
       " 'Female',\n",
       " 'Humans',\n",
       " 'Intensive Care Units/statistics & numerical data*',\n",
       " 'Medical Informatics Applications',\n",
       " 'Medical Records Systems, Computerized',\n",
       " 'Monitoring, Physiologic/instrumentation*',\n",
       " 'Quality Control',\n",
       " 'Retrospective Studies',\n",
       " 'United States',\n",
       " 'Algorithms*',\n",
       " 'Databases, Factual*',\n",
       " 'Humans',\n",
       " 'Intensive Care Units*',\n",
       " 'Intubation',\n",
       " 'Pressure',\n",
       " 'Respiration, Artificial*',\n",
       " 'Time Factors',\n",
       " 'Access to Information',\n",
       " 'Biomedical Research',\n",
       " 'Critical Care*',\n",
       " 'Databases, Factual',\n",
       " 'Humans',\n",
       " 'Internet',\n",
       " 'Software*',\n",
       " 'User-Computer Interface*',\n",
       " 'Aged',\n",
       " 'Critical Care',\n",
       " 'Databases, Factual*',\n",
       " 'Female',\n",
       " 'Hospital Mortality*',\n",
       " 'Humans',\n",
       " 'Intensive Care Units*',\n",
       " 'Male',\n",
       " 'Middle Aged',\n",
       " 'Norepinephrine/antagonists & inhibitors*',\n",
       " 'Patient Admission*',\n",
       " 'Retrospective Studies',\n",
       " 'Serotonin Uptake Inhibitors/therapeutic use*',\n",
       " 'Blood Pressure',\n",
       " 'Circadian Rhythm',\n",
       " 'Critical Care*',\n",
       " 'Data Mining*',\n",
       " 'Databases, Factual*',\n",
       " 'Heart Rate',\n",
       " 'Humans',\n",
       " 'Medical Informatics*',\n",
       " 'Aged',\n",
       " 'Cell Count',\n",
       " 'Erythrocyte Volume*',\n",
       " 'Female',\n",
       " 'Humans',\n",
       " 'Intensive Care Units*',\n",
       " 'Lymphocytes/cytology*',\n",
       " 'Male',\n",
       " 'Middle Aged',\n",
       " 'Neutrophils/cytology*',\n",
       " 'Patient Admission*',\n",
       " 'Prognosis',\n",
       " 'Retrospective Studies',\n",
       " 'Subarachnoid Hemorrhage/blood',\n",
       " 'Subarachnoid Hemorrhage/diagnosis*',\n",
       " 'Subarachnoid Hemorrhage/physiopathology*',\n",
       " 'Subarachnoid Hemorrhage/therapy',\n",
       " 'Adult',\n",
       " 'Classification/methods*',\n",
       " 'Electronic Health Records*/classification',\n",
       " 'Humans',\n",
       " 'Infant, Newborn',\n",
       " 'Information Storage and Retrieval*',\n",
       " 'Intensive Care Units',\n",
       " 'Jaundice, Neonatal/classification',\n",
       " 'Jaundice, Neonatal/diagnosis',\n",
       " 'Phototherapy/statistics & numerical data',\n",
       " 'Respiration, Artificial/statistics & numerical data',\n",
       " 'Support Vector Machine*',\n",
       " 'Aged',\n",
       " 'Calcium/chemistry*',\n",
       " 'Calcium/metabolism*',\n",
       " 'Critical Illness/mortality*',\n",
       " 'Databases, Factual*',\n",
       " 'Female',\n",
       " 'Humans',\n",
       " 'Intensive Care Units/statistics & numerical data',\n",
       " 'Logistic Models',\n",
       " 'Male',\n",
       " 'Medical Informatics/methods*',\n",
       " 'Middle Aged',\n",
       " 'Predictive Value of Tests',\n",
       " 'Blood Pressure',\n",
       " 'Critical Care',\n",
       " 'Forecasting',\n",
       " 'Heart Rate',\n",
       " 'Humans',\n",
       " 'Intensive Care Units*',\n",
       " 'Machine Learning*',\n",
       " 'Multiple Organ Failure',\n",
       " 'Risk Assessment/methods*',\n",
       " 'Shock, Septic*',\n",
       " 'Critical Care',\n",
       " 'Databases, Factual',\n",
       " 'Humans',\n",
       " 'Hyperlactatemia*',\n",
       " 'Intensive Care Units',\n",
       " 'Sepsis',\n",
       " 'Shock, Septic',\n",
       " 'Adult',\n",
       " 'Aged',\n",
       " 'Aged, 80 and over',\n",
       " 'Databases, Factual',\n",
       " 'Demography',\n",
       " 'Female',\n",
       " 'Humans',\n",
       " 'Intensive Care Units*',\n",
       " 'Linear Models',\n",
       " 'Male',\n",
       " 'Middle Aged',\n",
       " 'Models, Biological',\n",
       " 'Probability',\n",
       " 'Sepsis/diagnosis*',\n",
       " 'Sepsis/therapy*',\n",
       " 'Signal Processing, Computer-Assisted',\n",
       " 'Adult',\n",
       " 'Aged',\n",
       " 'Aged, 80 and over',\n",
       " 'Body Mass Index',\n",
       " 'Boston/epidemiology',\n",
       " 'Cohort Studies',\n",
       " 'Critical Illness/mortality*',\n",
       " 'Female',\n",
       " 'Humans',\n",
       " 'Intensive Care Units/statistics & numerical data*',\n",
       " 'Male',\n",
       " 'Middle Aged',\n",
       " 'Obesity/complications',\n",
       " 'Obesity/mortality*',\n",
       " 'Overweight/complications',\n",
       " 'Overweight/mortality*',\n",
       " 'Proportional Hazards Models',\n",
       " 'Risk Factors',\n",
       " 'Severity of Illness Index',\n",
       " 'Survival Analysis',\n",
       " 'Databases, Factual*',\n",
       " 'Electronic Health Records*',\n",
       " 'Humans',\n",
       " 'Medical Informatics Applications*',\n",
       " 'Clinical Laboratory Techniques/standards*',\n",
       " 'Databases, Factual/statistics & numerical data*',\n",
       " 'Humans',\n",
       " 'Information Theory*',\n",
       " 'Intensive Care Units/standards*',\n",
       " 'Unnecessary Procedures/standards*',\n",
       " 'Access to Information*',\n",
       " 'Adult',\n",
       " 'Critical Care*',\n",
       " 'Databases, Factual*',\n",
       " 'Humans',\n",
       " 'Monitoring, Physiologic*',\n",
       " 'Research*',\n",
       " 'Wavelet Analysis',\n",
       " 'Algorithms',\n",
       " 'Blood Pressure/physiology*',\n",
       " 'Data Mining/methods*',\n",
       " 'Humans',\n",
       " 'Hypotension/diagnosis*',\n",
       " 'Hypotension/prevention & control*',\n",
       " 'Intensive Care Units*',\n",
       " 'Pattern Recognition, Automated/methods*',\n",
       " 'Risk Factors',\n",
       " 'Algorithms',\n",
       " 'Biomarkers',\n",
       " 'Databases, Factual',\n",
       " 'Fuzzy Logic*',\n",
       " 'Heart Failure/diagnosis*',\n",
       " 'Heart Failure/etiology',\n",
       " 'Heart Failure/physiopathology*',\n",
       " 'Hemodynamics',\n",
       " 'Humans',\n",
       " 'Intensive Care Units*',\n",
       " 'Models, Theoretical*',\n",
       " 'Patient Admission',\n",
       " 'Prognosis',\n",
       " 'Retrospective Studies',\n",
       " 'Severity of Illness Index',\n",
       " 'Stroke Volume*',\n",
       " 'Ventricular Function, Left*',\n",
       " 'Algorithms',\n",
       " 'Artifacts',\n",
       " 'Critical Care',\n",
       " 'Humans',\n",
       " 'Hypovolemia*',\n",
       " 'Photoplethysmography',\n",
       " 'Signal Processing, Computer-Assisted',\n",
       " 'Boston',\n",
       " 'Critical Care*',\n",
       " 'Electronic Health Records/statistics & numerical data*',\n",
       " 'Humans',\n",
       " 'Intensive Care Units*/organization & administration',\n",
       " 'Middle Aged',\n",
       " 'Prognosis',\n",
       " 'Quality Assurance, Health Care',\n",
       " 'Reproducibility of Results',\n",
       " 'Retrospective Studies',\n",
       " 'Severity of Illness Index*',\n",
       " 'Data Mining/methods*',\n",
       " 'Electronic Health Records/classification*',\n",
       " 'Humans',\n",
       " 'Intensive Care Units*',\n",
       " 'Medical Informatics Applications*',\n",
       " 'Nurses',\n",
       " 'Regression Analysis',\n",
       " 'Risk Assessment',\n",
       " 'Acute Kidney Injury/mortality*',\n",
       " 'Critical Care',\n",
       " 'Hospital Mortality*',\n",
       " 'Humans',\n",
       " 'Intensive Care Units',\n",
       " 'Prognosis',\n",
       " 'ROC Curve',\n",
       " 'Sensitivity and Specificity',\n",
       " 'Support Vector Machine*',\n",
       " 'Fuzzy Logic',\n",
       " 'Humans',\n",
       " 'Intensive Care Units/organization & administration*',\n",
       " 'Monitoring, Physiologic/methods*',\n",
       " 'Risk Assessment*',\n",
       " 'Algorithms',\n",
       " 'Critical Care/statistics & numerical data*',\n",
       " 'Critical Illness/therapy*',\n",
       " 'Data Mining/methods',\n",
       " 'Data Mining/statistics & numerical data*',\n",
       " 'Databases, Factual/statistics & numerical data',\n",
       " 'Humans',\n",
       " 'Information Storage and Retrieval/methods',\n",
       " 'Information Storage and Retrieval/statistics & numerical data*',\n",
       " 'Intensive Care Units/statistics & numerical data',\n",
       " 'Models, Theoretical',\n",
       " 'Programming Languages',\n",
       " 'Reproducibility of Results',\n",
       " 'Computational Biology/methods',\n",
       " 'Critical Care/statistics & numerical data*',\n",
       " 'Databases, Factual/statistics & numerical data',\n",
       " 'Electronic Health Records/statistics & numerical data',\n",
       " 'Female',\n",
       " 'Humans',\n",
       " 'Machine Learning/statistics & numerical data*',\n",
       " 'Male',\n",
       " 'Supervised Machine Learning/statistics & numerical data',\n",
       " 'Unsupervised Machine Learning/statistics & numerical data',\n",
       " 'Algorithms',\n",
       " 'Clinical Alarms*',\n",
       " 'Critical Care/methods*',\n",
       " 'Diagnosis, Computer-Assisted/methods*',\n",
       " 'Diagnostic Errors/prevention & control*',\n",
       " 'Electrocardiography/methods*',\n",
       " 'False Positive Reactions',\n",
       " 'Humans',\n",
       " 'Intensive Care Units',\n",
       " 'Pattern Recognition, Automated/methods',\n",
       " 'Reproducibility of Results',\n",
       " 'Sensitivity and Specificity',\n",
       " 'Tachycardia, Ventricular/diagnosis*',\n",
       " 'Wavelet Analysis',\n",
       " 'Artificial Intelligence*',\n",
       " 'Boston',\n",
       " 'Decision Making, Computer-Assisted',\n",
       " 'Female',\n",
       " 'Fluid Therapy*',\n",
       " 'Humans',\n",
       " 'Information Storage and Retrieval',\n",
       " 'Intensive Care Units*',\n",
       " 'Male',\n",
       " 'Vasoconstrictor Agents/therapeutic use',\n",
       " 'Adult',\n",
       " 'Aged',\n",
       " 'Algorithms',\n",
       " 'Computational Biology',\n",
       " 'Disseminated Intravascular Coagulation*',\n",
       " 'Electronic Health Records*',\n",
       " 'Female',\n",
       " 'Humans',\n",
       " 'Information Storage and Retrieval',\n",
       " 'Male',\n",
       " 'Middle Aged',\n",
       " 'Patient-Specific Modeling*',\n",
       " 'Prospective Studies',\n",
       " 'Retrospective Studies',\n",
       " 'Software*',\n",
       " 'Artificial Intelligence*',\n",
       " 'Databases, Factual*',\n",
       " 'Decision Support Systems, Clinical*',\n",
       " 'Expert Systems*',\n",
       " 'Humans',\n",
       " 'Information Systems',\n",
       " 'Intensive Care Units*',\n",
       " 'Medical Records Systems, Computerized',\n",
       " 'Monitoring, Physiologic/instrumentation*',\n",
       " 'Signal Processing, Computer-Assisted',\n",
       " 'Software',\n",
       " 'Acute Disease',\n",
       " 'Algorithms',\n",
       " 'Databases, Factual',\n",
       " 'Diagnosis, Computer-Assisted/methods*',\n",
       " 'Humans',\n",
       " 'Hypotension/diagnosis*',\n",
       " 'Hypotension/physiopathology',\n",
       " 'Intensive Care Units',\n",
       " 'Neural Networks (Computer)*',\n",
       " 'Predictive Value of Tests',\n",
       " 'Regression Analysis',\n",
       " 'Reproducibility of Results',\n",
       " 'Signal Processing, Computer-Assisted*',\n",
       " 'Humans',\n",
       " 'Intensive Care Units*',\n",
       " 'Logistic Models',\n",
       " 'Natural Language Processing',\n",
       " 'Resuscitation*',\n",
       " 'Adult',\n",
       " 'Algorithms',\n",
       " 'Brain Injuries/epidemiology',\n",
       " 'Child',\n",
       " 'Critical Care',\n",
       " 'Databases, Factual',\n",
       " 'Hospital Mortality*',\n",
       " 'Humans',\n",
       " 'Intensive Care Units',\n",
       " 'Intensive Care Units, Pediatric',\n",
       " 'Medical Informatics/methods*',\n",
       " 'Models, Statistical',\n",
       " 'Outcome Assessment (Health Care)',\n",
       " 'Predictive Value of Tests',\n",
       " 'Prognosis',\n",
       " 'ROC Curve',\n",
       " 'Regression Analysis',\n",
       " 'Risk Assessment/methods*',\n",
       " 'Access to Information',\n",
       " 'Biomedical Research*',\n",
       " 'Comorbidity',\n",
       " 'Critical Care*',\n",
       " 'Data Mining',\n",
       " 'Databases, Factual*',\n",
       " 'Electronic Health Records',\n",
       " 'Humans',\n",
       " 'Organ Dysfunction Scores',\n",
       " 'ROC Curve',\n",
       " 'Reproducibility of Results',\n",
       " 'Sepsis',\n",
       " 'Severity of Illness Index',\n",
       " 'Therapeutics',\n",
       " 'Algorithms',\n",
       " 'Databases, Factual',\n",
       " 'Humans',\n",
       " 'Intensive Care Units*',\n",
       " 'Acute Disease',\n",
       " 'Aged',\n",
       " 'Critical Illness/mortality*',\n",
       " 'Databases, Factual',\n",
       " 'Female',\n",
       " 'Humans',\n",
       " 'Intensive Care Units',\n",
       " 'Isotonic Solutions/administration & dosage*',\n",
       " 'Logistic Models',\n",
       " 'Male',\n",
       " 'Massachusetts/epidemiology',\n",
       " 'Middle Aged',\n",
       " 'Pancreatitis/etiology',\n",
       " 'Pancreatitis/mortality*',\n",
       " 'Pancreatitis/therapy*',\n",
       " 'Resuscitation/methods*',\n",
       " 'Retrospective Studies',\n",
       " \"Ringer's Lactate\",\n",
       " 'Sodium Chloride/administration & dosage*',\n",
       " 'Adult',\n",
       " 'Aged',\n",
       " 'Area Under Curve',\n",
       " 'Cross-Sectional Studies',\n",
       " 'Female',\n",
       " 'Humans',\n",
       " 'Intensive Care Units/statistics & numerical data*',\n",
       " 'Machine Learning*',\n",
       " 'Male',\n",
       " 'Middle Aged',\n",
       " 'Patient Readmission/statistics & numerical data*',\n",
       " 'ROC Curve',\n",
       " 'Retrospective Studies',\n",
       " 'Tertiary Care Centers',\n",
       " 'United Kingdom',\n",
       " 'Adult',\n",
       " 'Algorithms',\n",
       " 'Area Under Curve',\n",
       " 'Hospital Mortality',\n",
       " 'Humans',\n",
       " 'Intensive Care Units*',\n",
       " 'Models, Theoretical',\n",
       " 'Natural Language Processing*',\n",
       " 'Nursing Records*',\n",
       " 'Risk Assessment/methods*',\n",
       " 'Severity of Illness Index',\n",
       " 'Unified Medical Language System',\n",
       " 'Adult',\n",
       " 'Aged',\n",
       " 'Critical Illness',\n",
       " 'Diabetic Ketoacidosis/blood*',\n",
       " 'Diabetic Ketoacidosis/mortality*',\n",
       " 'Female',\n",
       " 'Humans',\n",
       " 'Intensive Care Units',\n",
       " 'Kaplan-Meier Estimate',\n",
       " 'Longitudinal Studies',\n",
       " 'Lymphocyte Count',\n",
       " 'Male',\n",
       " 'Middle Aged',\n",
       " 'Patient Readmission*',\n",
       " 'Platelet Count',\n",
       " 'Predictive Value of Tests',\n",
       " 'Prognosis',\n",
       " 'Retrospective Studies',\n",
       " 'Time Factors',\n",
       " 'Airway Extubation/statistics & numerical data*',\n",
       " 'Algorithms',\n",
       " 'Artificial Intelligence*',\n",
       " 'Database Management Systems',\n",
       " 'Databases, Factual*',\n",
       " 'Decision Support Systems, Management*',\n",
       " 'Diagnosis, Computer-Assisted/methods',\n",
       " 'Female',\n",
       " 'Humans',\n",
       " 'Incidence',\n",
       " 'Infant, Newborn',\n",
       " 'Male',\n",
       " 'New York/epidemiology',\n",
       " 'Prognosis',\n",
       " 'Reproducibility of Results',\n",
       " 'Respiration, Artificial/statistics & numerical data*',\n",
       " 'Respiratory Distress Syndrome, Newborn/diagnosis*',\n",
       " 'Respiratory Distress Syndrome, Newborn/epidemiology',\n",
       " 'Respiratory Distress Syndrome, Newborn/rehabilitation*',\n",
       " 'Risk Assessment',\n",
       " 'Sensitivity and Specificity',\n",
       " 'Treatment Failure',\n",
       " 'Critical Care',\n",
       " 'Humans',\n",
       " 'Intensive Care Units',\n",
       " 'Length of Stay',\n",
       " 'Logistic Models',\n",
       " 'Patient Readmission*',\n",
       " 'Retrospective Studies',\n",
       " 'Algorithms',\n",
       " 'Blood Pressure',\n",
       " 'Heart Rate/physiology',\n",
       " 'Hemodynamics/physiology*',\n",
       " 'Humans',\n",
       " 'Intensive Care Units/statistics & numerical data*',\n",
       " 'Monitoring, Physiologic',\n",
       " 'Multiple Organ Failure/diagnosis*',\n",
       " 'Multiple Organ Failure/physiopathology*',\n",
       " 'Neural Networks (Computer)',\n",
       " 'ROC Curve',\n",
       " 'Regression Analysis',\n",
       " 'Reproducibility of Results',\n",
       " 'Sensitivity and Specificity',\n",
       " 'Software',\n",
       " 'Time Factors',\n",
       " 'Humans',\n",
       " 'Hypotension/diagnosis*',\n",
       " 'Intensive Care Units',\n",
       " 'Nonlinear Dynamics',\n",
       " 'Software',\n",
       " 'Heart Rate/physiology*',\n",
       " 'Humans',\n",
       " 'Intensive Care Units',\n",
       " 'Internet',\n",
       " 'Sepsis/diagnosis',\n",
       " 'Sepsis/pathology*',\n",
       " 'User-Computer Interface',\n",
       " 'Vital Signs/physiology',\n",
       " 'Algorithms',\n",
       " 'Decision Trees',\n",
       " 'Female',\n",
       " 'Hospital Mortality*',\n",
       " 'Humans',\n",
       " 'Intensive Care Units',\n",
       " 'Logistic Models',\n",
       " 'Male',\n",
       " 'Multivariate Analysis',\n",
       " 'Respiration Disorders/mortality*',\n",
       " 'Respiration, Artificial*',\n",
       " 'Time Factors',\n",
       " 'Adult',\n",
       " 'Blood Pressure/physiology',\n",
       " 'Critical Care/methods*',\n",
       " 'Humans',\n",
       " 'Monitoring, Physiologic/methods*',\n",
       " 'Vasoconstrictor Agents*/administration & dosage',\n",
       " 'Vasoconstrictor Agents*/therapeutic use',\n",
       " 'Aged',\n",
       " 'Algorithms',\n",
       " 'Critical Care/methods*',\n",
       " 'Critical Illness',\n",
       " 'Electronic Data Processing',\n",
       " 'Female',\n",
       " 'Health Status',\n",
       " 'Humans',\n",
       " 'Intensive Care Units*',\n",
       " 'Least-Squares Analysis',\n",
       " 'Linear Models',\n",
       " 'Machine Learning',\n",
       " 'Male',\n",
       " 'Medical Informatics',\n",
       " 'Middle Aged',\n",
       " 'Monitoring, Ambulatory/methods*',\n",
       " 'Principal Component Analysis',\n",
       " 'Reproducibility of Results',\n",
       " 'Sensitivity and Specificity',\n",
       " 'Signal Processing, Computer-Assisted',\n",
       " 'Vital Signs',\n",
       " 'Adult',\n",
       " 'Algorithms',\n",
       " 'Blood Pressure/physiology',\n",
       " 'Databases, Factual',\n",
       " 'Female',\n",
       " 'Health Status Indicators*',\n",
       " 'Heart Rate/physiology',\n",
       " 'Hospital Mortality',\n",
       " 'Humans',\n",
       " 'Intensive Care Units',\n",
       " 'Male',\n",
       " 'Medical Informatics',\n",
       " 'Models, Statistical*',\n",
       " 'Monitoring, Physiologic/methods*',\n",
       " 'Prognosis',\n",
       " 'Reproducibility of Results',\n",
       " 'Tilt-Table Test',\n",
       " 'Adult',\n",
       " 'Datasets as Topic',\n",
       " 'Decision Making, Computer-Assisted*',\n",
       " 'Decision Support Systems, Clinical',\n",
       " 'Humans',\n",
       " 'Intensive Care Units',\n",
       " 'Length of Stay',\n",
       " 'Patient Discharge*',\n",
       " 'Prognosis',\n",
       " 'ROC Curve',\n",
       " 'Retrospective Studies',\n",
       " 'Risk Assessment/methods',\n",
       " 'Risk Factors',\n",
       " 'Triage/methods',\n",
       " 'Data Mining*',\n",
       " 'Databases, Factual',\n",
       " 'Humans',\n",
       " 'Intensive Care Units*',\n",
       " 'Regression Analysis*',\n",
       " 'Support Vector Machine*',\n",
       " 'Critical Care/methods*',\n",
       " 'Databases, Factual',\n",
       " 'Humans',\n",
       " 'Medical Records',\n",
       " 'Nurses',\n",
       " 'Postoperative Complications/diagnosis*',\n",
       " 'Postoperative Complications/epidemiology*',\n",
       " 'Respiratory Distress Syndrome, Adult/diagnosis*',\n",
       " 'Respiratory Distress Syndrome, Adult/epidemiology*',\n",
       " 'Respiratory Rate/physiology*',\n",
       " 'Risk',\n",
       " 'Adult',\n",
       " 'Critical Illness',\n",
       " 'Cross Infection/diagnosis*',\n",
       " 'Electronic Health Records*',\n",
       " 'Humans',\n",
       " 'Iatrogenic Disease',\n",
       " 'Intensive Care Units/organization & administration',\n",
       " 'International Classification of Diseases',\n",
       " 'Phenotype*',\n",
       " 'Time',\n",
       " 'Critical Care*',\n",
       " 'Data Interpretation, Statistical',\n",
       " 'Hemodynamics*',\n",
       " 'Humans',\n",
       " 'Hypotension/diagnosis*',\n",
       " 'Hypotension/physiopathology*',\n",
       " 'Pattern Recognition, Automated/methods*',\n",
       " 'Regression Analysis',\n",
       " 'Acute Disease',\n",
       " 'Algorithms',\n",
       " 'Blood Pressure/physiology',\n",
       " 'Databases, Factual',\n",
       " 'Early Diagnosis*',\n",
       " 'Humans',\n",
       " 'Intensive Care Units',\n",
       " 'Models, Theoretical',\n",
       " 'Sensitivity and Specificity',\n",
       " 'Anti-Bacterial Agents/therapeutic use',\n",
       " 'Clostridium Infections/drug therapy',\n",
       " 'Clostridium Infections/immunology*',\n",
       " 'Clostridium difficile*',\n",
       " 'Critical Illness',\n",
       " 'Delayed Diagnosis',\n",
       " 'Electronic Health Records',\n",
       " 'Genomics',\n",
       " 'Humans',\n",
       " 'Intensive Care Units',\n",
       " 'International Classification of Diseases',\n",
       " 'Leukocyte Count*',\n",
       " 'Leukocytosis/etiology',\n",
       " 'Medical Records Systems, Computerized',\n",
       " 'Phenotype*',\n",
       " 'Reference Values',\n",
       " 'Acute Disease',\n",
       " 'Algorithms',\n",
       " 'Biomedical Engineering/methods',\n",
       " 'Blood Pressure',\n",
       " 'Cardiology/methods',\n",
       " 'Humans',\n",
       " 'Hypotension/diagnosis*',\n",
       " 'Hypotension/physiopathology*',\n",
       " 'Neural Networks (Computer)',\n",
       " 'Signal Processing, Computer-Assisted*',\n",
       " 'Software',\n",
       " 'Time Factors',\n",
       " 'Adolescent',\n",
       " 'Adult',\n",
       " 'Aged',\n",
       " 'Algorithms',\n",
       " 'Child',\n",
       " 'Child, Preschool',\n",
       " 'Electrocardiography*',\n",
       " 'Fourier Analysis',\n",
       " 'Humans',\n",
       " 'Infant',\n",
       " 'Intensive Care Units',\n",
       " 'Middle Aged',\n",
       " 'Plethysmography*',\n",
       " 'Respiratory Rate/physiology*',\n",
       " 'Signal Processing, Computer-Assisted',\n",
       " 'Young Adult',\n",
       " 'Algorithms*',\n",
       " 'Critical Care/methods*',\n",
       " 'Decision Support Systems, Clinical*',\n",
       " 'Diagnosis, Computer-Assisted/methods*',\n",
       " 'Hypotension/diagnosis*',\n",
       " 'Monitoring, Physiologic/methods*',\n",
       " 'Multiple Organ Failure/diagnosis*',\n",
       " 'New York',\n",
       " 'Prognosis',\n",
       " 'Reproducibility of Results',\n",
       " 'Sensitivity and Specificity',\n",
       " 'Algorithms',\n",
       " 'Hospital Mortality',\n",
       " 'Humans',\n",
       " 'Intensive Care Units',\n",
       " 'Markov Chains*',\n",
       " 'Monitoring, Physiologic/methods*',\n",
       " 'Oxygen/metabolism',\n",
       " 'Partial Pressure',\n",
       " 'Pattern Recognition, Automated',\n",
       " 'Respiration Disorders/diagnosis*',\n",
       " 'Respiration Disorders/mortality',\n",
       " 'Respiration Disorders/physiopathology*',\n",
       " 'Respiratory Rate/physiology',\n",
       " 'Algorithms*',\n",
       " 'Databases, Factual',\n",
       " 'False Positive Reactions',\n",
       " 'Heart/physiology*',\n",
       " 'Heart Function Tests/methods*',\n",
       " 'Heart Rate*',\n",
       " 'Humans',\n",
       " 'Sensitivity and Specificity',\n",
       " 'Software',\n",
       " 'Adult',\n",
       " 'Aged',\n",
       " 'Aged, 80 and over',\n",
       " 'Antipyretics/adverse effects*',\n",
       " 'Antipyretics/therapeutic use*',\n",
       " 'Body Temperature/drug effects*',\n",
       " 'Body Temperature/physiology',\n",
       " 'Critical Care',\n",
       " 'Critical Illness',\n",
       " 'Cryotherapy/adverse effects',\n",
       " 'Databases, Factual',\n",
       " 'Female',\n",
       " 'Humans',\n",
       " 'Male',\n",
       " 'Middle Aged',\n",
       " 'Multivariate Analysis',\n",
       " 'Probability',\n",
       " 'Risk Factors',\n",
       " 'Sepsis/drug therapy*',\n",
       " 'Sepsis/mortality',\n",
       " 'Sepsis/physiopathology*',\n",
       " 'Computer Simulation*',\n",
       " 'Decision Support Techniques*',\n",
       " 'Electronic Health Records*',\n",
       " 'Humans',\n",
       " 'Models, Statistical',\n",
       " 'Phenotype*',\n",
       " 'Algorithms*',\n",
       " 'Blood Pressure Determination/methods*',\n",
       " 'Computer Simulation',\n",
       " 'Critical Care/methods',\n",
       " 'Diagnosis, Computer-Assisted/methods*',\n",
       " 'Electrocardiography/methods*',\n",
       " 'Humans',\n",
       " 'Hypotension/diagnosis*',\n",
       " 'Models, Cardiovascular*',\n",
       " 'Reproducibility of Results',\n",
       " 'Risk Assessment/methods',\n",
       " 'Risk Factors',\n",
       " 'Sensitivity and Specificity',\n",
       " 'Acute Kidney Injury/blood*',\n",
       " 'Acute Kidney Injury/mortality',\n",
       " 'Acute Kidney Injury/physiopathology',\n",
       " 'Adult',\n",
       " 'Age Factors',\n",
       " 'Aged',\n",
       " 'Alanine Transaminase/blood',\n",
       " 'Bilirubin/blood',\n",
       " 'Biomarkers/blood',\n",
       " 'Critical Illness/mortality*',\n",
       " 'Female',\n",
       " 'Hepatitis/blood*',\n",
       " 'Hepatitis/mortality',\n",
       " 'Hepatitis/physiopathology',\n",
       " 'Hospital Mortality',\n",
       " 'Humans',\n",
       " 'Hypoxia/blood*',\n",
       " 'Hypoxia/mortality',\n",
       " 'Hypoxia/physiopathology',\n",
       " 'L-Lactate Dehydrogenase/blood',\n",
       " 'Male',\n",
       " 'Middle Aged',\n",
       " 'Predictive Value of Tests',\n",
       " 'Retrospective Studies',\n",
       " 'Risk Factors',\n",
       " 'Vasoconstrictor Agents/therapeutic use',\n",
       " 'Adult',\n",
       " 'Aged',\n",
       " 'Aged, 80 and over',\n",
       " 'Critical Care',\n",
       " 'Critical Illness/mortality',\n",
       " 'Electronic Health Records/statistics & numerical data*',\n",
       " 'Hospital Mortality*',\n",
       " 'Humans',\n",
       " 'Medical Informatics/methods*',\n",
       " 'Middle Aged',\n",
       " 'ROC Curve',\n",
       " 'Regression Analysis*',\n",
       " 'Sepsis/diagnosis',\n",
       " 'Sepsis/epidemiology',\n",
       " 'Sepsis/mortality*',\n",
       " 'Young Adult',\n",
       " 'Equipment Failure*',\n",
       " 'Humans',\n",
       " 'Information Storage and Retrieval*',\n",
       " 'Monitoring, Physiologic/instrumentation*',\n",
       " 'Algorithms*',\n",
       " 'Arrhythmias, Cardiac/diagnosis*',\n",
       " 'Artificial Intelligence',\n",
       " 'Blood Pressure Determination/methods*',\n",
       " 'Critical Care/methods*',\n",
       " 'Diagnosis, Computer-Assisted/methods*',\n",
       " 'Electrocardiography/methods*',\n",
       " 'False Positive Reactions',\n",
       " 'Humans',\n",
       " 'Pattern Recognition, Automated/methods*',\n",
       " 'Reproducibility of Results',\n",
       " 'Sensitivity and Specificity',\n",
       " 'Aged',\n",
       " 'Aged, 80 and over',\n",
       " 'Antipyretics/adverse effects*',\n",
       " 'Boston/epidemiology',\n",
       " 'Critical Illness/mortality*',\n",
       " 'Female',\n",
       " 'Fever/drug therapy*',\n",
       " 'Fever/etiology',\n",
       " 'Humans',\n",
       " 'Male',\n",
       " 'Middle Aged',\n",
       " 'Sepsis/complications*',\n",
       " 'Sepsis/mortality',\n",
       " 'Aged',\n",
       " 'Area Under Curve',\n",
       " 'Arterial Pressure',\n",
       " 'Bilirubin/blood',\n",
       " 'Biomarkers/blood',\n",
       " 'Creatinine/blood',\n",
       " 'Critical Illness',\n",
       " 'Databases, Factual',\n",
       " 'Decision Support Techniques*',\n",
       " 'Female',\n",
       " 'Humans',\n",
       " 'International Normalized Ratio',\n",
       " 'Kaplan-Meier Estimate',\n",
       " 'Liver Cirrhosis/blood',\n",
       " 'Liver Cirrhosis/complications',\n",
       " 'Liver Cirrhosis/diagnosis*',\n",
       " 'Liver Cirrhosis/mortality*',\n",
       " 'Liver Failure/blood',\n",
       " 'Liver Failure/diagnosis*',\n",
       " 'Liver Failure/etiology',\n",
       " 'Liver Failure/mortality*',\n",
       " 'Male',\n",
       " 'Middle Aged',\n",
       " 'Models, Biological*',\n",
       " 'Multiple Organ Failure/blood',\n",
       " 'Multiple Organ Failure/diagnosis*',\n",
       " 'Multiple Organ Failure/etiology',\n",
       " 'Multiple Organ Failure/mortality*',\n",
       " 'Organ Dysfunction Scores*',\n",
       " 'Predictive Value of Tests',\n",
       " 'Prognosis',\n",
       " 'ROC Curve',\n",
       " 'Reproducibility of Results',\n",
       " 'Risk Assessment',\n",
       " 'Risk Factors',\n",
       " 'Time Factors',\n",
       " 'Blood Glucose/analysis*',\n",
       " 'Critical Illness/classification*',\n",
       " 'Critical Illness/mortality',\n",
       " 'Female',\n",
       " 'Humans',\n",
       " 'Hyperglycemia/diagnosis',\n",
       " 'Hypoglycemia/diagnosis',\n",
       " 'Male',\n",
       " 'Middle Aged',\n",
       " 'Organ Dysfunction Scores',\n",
       " 'Prognosis',\n",
       " 'Proportional Hazards Models',\n",
       " 'Reproducibility of Results',\n",
       " 'Severity of Illness Index*',\n",
       " 'Arrhythmias, Cardiac/diagnosis',\n",
       " 'Arrhythmias, Cardiac/physiopathology*',\n",
       " 'Clinical Alarms*',\n",
       " 'Costs and Cost Analysis*',\n",
       " 'Electrocardiography/instrumentation*',\n",
       " 'False Positive Reactions',\n",
       " 'Heart Rate*',\n",
       " 'Humans',\n",
       " 'Signal Processing, Computer-Assisted',\n",
       " 'Support Vector Machine/economics*',\n",
       " 'Time Factors',\n",
       " 'Adult',\n",
       " 'Alcoholism/mortality*',\n",
       " 'Female',\n",
       " 'Humans',\n",
       " 'Male',\n",
       " 'Middle Aged',\n",
       " 'Models, Biological*',\n",
       " 'Predictive Value of Tests',\n",
       " 'Software*',\n",
       " 'Time Factors',\n",
       " 'Triage/methods*',\n",
       " 'Algorithms',\n",
       " 'Electronic Health Records',\n",
       " 'Humans',\n",
       " 'Models, Theoretical*',\n",
       " 'Mortality*',\n",
       " 'Time Factors',\n",
       " 'Algorithms',\n",
       " 'Cluster Analysis',\n",
       " 'Databases, Factual*',\n",
       " 'Aged',\n",
       " 'Critical Illness/mortality',\n",
       " 'Female',\n",
       " 'Health Status Indicators',\n",
       " 'Humans',\n",
       " 'Liver Cirrhosis/diagnosis',\n",
       " 'Liver Cirrhosis/mortality*',\n",
       " 'Male',\n",
       " 'Middle Aged',\n",
       " 'Prognosis',\n",
       " 'Respiratory Insufficiency/diagnosis',\n",
       " 'Respiratory Insufficiency/mortality*',\n",
       " 'Retrospective Studies',\n",
       " 'Risk Assessment',\n",
       " 'Severity of Illness Index*',\n",
       " 'Algorithms',\n",
       " 'Databases, Factual',\n",
       " 'Humans',\n",
       " 'Photoplethysmography/methods*',\n",
       " 'Signal Processing, Computer-Assisted*',\n",
       " 'Ventricular Premature Complexes/diagnosis*',\n",
       " 'Adult',\n",
       " 'Computational Biology',\n",
       " 'Databases, Factual',\n",
       " 'Hemodynamics',\n",
       " 'Humans',\n",
       " 'Models, Biological',\n",
       " 'Multivariate Analysis',\n",
       " 'Prognosis',\n",
       " 'Shock, Septic/mortality*',\n",
       " 'Shock, Septic/physiopathology',\n",
       " 'Aged',\n",
       " 'Aged, 80 and over',\n",
       " 'Critical Care',\n",
       " 'Databases, Factual',\n",
       " 'Humans',\n",
       " 'Middle Aged',\n",
       " 'Mortality',\n",
       " 'Patient Discharge*',\n",
       " 'Pattern Recognition, Automated',\n",
       " 'Prognosis',\n",
       " 'Algorithms',\n",
       " 'Blood Pressure',\n",
       " 'Electrocardiography',\n",
       " 'Heart Ventricles/pathology',\n",
       " 'Heart Ventricles/physiopathology',\n",
       " 'Hemodynamics*',\n",
       " 'Humans',\n",
       " 'Monitoring, Physiologic/instrumentation*',\n",
       " 'Tachycardia, Ventricular/physiopathology*',\n",
       " 'Wavelet Analysis',\n",
       " 'Algorithms',\n",
       " 'Databases, Factual',\n",
       " 'Electronic Health Records*',\n",
       " 'Female',\n",
       " 'Humans',\n",
       " 'Infant, Newborn*',\n",
       " 'Massachusetts',\n",
       " 'Mothers*',\n",
       " 'Pattern Recognition, Automated*',\n",
       " 'Algorithms',\n",
       " 'Datasets as Topic',\n",
       " 'Electronic Health Records/organization & administration*',\n",
       " 'Electronic Health Records/standards',\n",
       " 'Health Information Exchange/standards*',\n",
       " 'Health Information Interoperability/standards',\n",
       " 'Health Level Seven',\n",
       " 'Humans',\n",
       " 'Internet',\n",
       " 'Pilot Projects',\n",
       " 'Software*',\n",
       " 'Algorithms',\n",
       " 'Databases, Factual',\n",
       " 'Dose-Response Relationship, Drug',\n",
       " 'Heparin/administration & dosage*',\n",
       " 'Heparin/pharmacology*',\n",
       " 'Humans',\n",
       " 'Learning*',\n",
       " 'Length of Stay',\n",
       " 'Markov Chains',\n",
       " 'Reinforcement (Psychology)*',\n",
       " 'Retrospective Studies',\n",
       " 'Bayes Theorem',\n",
       " 'Data Mining*',\n",
       " 'Humans',\n",
       " 'Knowledge Bases',\n",
       " 'Linguistics',\n",
       " 'Medical Informatics/methods',\n",
       " ...]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_mesh_terms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Academic Medical Centers',\n",
       " 'Access to Information',\n",
       " 'Access to Information*',\n",
       " 'Acute Disease',\n",
       " 'Acute Kidney Injury/blood*',\n",
       " 'Acute Kidney Injury/mortality',\n",
       " 'Acute Kidney Injury/mortality*',\n",
       " 'Acute Kidney Injury/physiopathology',\n",
       " 'Adolescent',\n",
       " 'Adult',\n",
       " 'African Americans/statistics & numerical data',\n",
       " 'Age Factors',\n",
       " 'Aged',\n",
       " 'Aged, 80 and over',\n",
       " 'Airway Extubation/statistics & numerical data*',\n",
       " 'Alanine Transaminase/blood',\n",
       " 'Alcoholism/mortality*',\n",
       " 'Algorithms',\n",
       " 'Algorithms*',\n",
       " 'Anti-Bacterial Agents/therapeutic use',\n",
       " 'Antipyretics/adverse effects*',\n",
       " 'Antipyretics/therapeutic use*',\n",
       " 'Area Under Curve',\n",
       " 'Arrhythmias, Cardiac/diagnosis',\n",
       " 'Arrhythmias, Cardiac/diagnosis*',\n",
       " 'Arrhythmias, Cardiac/physiopathology*',\n",
       " 'Arterial Pressure',\n",
       " 'Artifacts',\n",
       " 'Artificial Intelligence',\n",
       " 'Artificial Intelligence*',\n",
       " 'Asian Americans/statistics & numerical data',\n",
       " 'Bayes Theorem',\n",
       " 'Bilirubin/blood',\n",
       " 'Biomarkers',\n",
       " 'Biomarkers/blood',\n",
       " 'Biomedical Engineering/methods',\n",
       " 'Biomedical Research',\n",
       " 'Biomedical Research*',\n",
       " 'Blood Glucose/analysis*',\n",
       " 'Blood Pressure',\n",
       " 'Blood Pressure Determination/methods*',\n",
       " 'Blood Pressure/physiology',\n",
       " 'Blood Pressure/physiology*',\n",
       " 'Body Mass Index',\n",
       " 'Body Temperature/drug effects*',\n",
       " 'Body Temperature/physiology',\n",
       " 'Boston',\n",
       " 'Boston/epidemiology',\n",
       " 'Brain Injuries/epidemiology',\n",
       " 'Brain Ischemia/classification',\n",
       " 'Calcium/chemistry*',\n",
       " 'Calcium/metabolism*',\n",
       " 'Cardiology/methods',\n",
       " 'Cell Count',\n",
       " 'Child',\n",
       " 'Child, Preschool',\n",
       " 'Circadian Rhythm',\n",
       " 'Classification/methods*',\n",
       " 'Clinical Alarms*',\n",
       " 'Clinical Coding/methods*',\n",
       " 'Clinical Laboratory Techniques/standards*',\n",
       " 'Clostridium Infections/drug therapy',\n",
       " 'Clostridium Infections/immunology*',\n",
       " 'Clostridium difficile*',\n",
       " 'Cluster Analysis',\n",
       " 'Cohort Studies',\n",
       " 'Comorbidity',\n",
       " 'Computational Biology',\n",
       " 'Computational Biology/methods',\n",
       " 'Computer Simulation',\n",
       " 'Computer Simulation*',\n",
       " 'Continental Population Groups/statistics & numerical data*',\n",
       " 'Costs and Cost Analysis*',\n",
       " 'Creatinine/blood',\n",
       " 'Critical Care',\n",
       " 'Critical Care*',\n",
       " 'Critical Care/methods',\n",
       " 'Critical Care/methods*',\n",
       " 'Critical Care/statistics & numerical data*',\n",
       " 'Critical Illness',\n",
       " 'Critical Illness/classification*',\n",
       " 'Critical Illness/mortality',\n",
       " 'Critical Illness/mortality*',\n",
       " 'Critical Illness/therapy*',\n",
       " 'Cross Infection/diagnosis*',\n",
       " 'Cross-Sectional Studies',\n",
       " 'Cryotherapy/adverse effects',\n",
       " 'Data Interpretation, Statistical',\n",
       " 'Data Mining',\n",
       " 'Data Mining*',\n",
       " 'Data Mining/methods',\n",
       " 'Data Mining/methods*',\n",
       " 'Data Mining/statistics & numerical data*',\n",
       " 'Database Management Systems',\n",
       " 'Databases, Factual',\n",
       " 'Databases, Factual*',\n",
       " 'Databases, Factual/statistics & numerical data',\n",
       " 'Databases, Factual/statistics & numerical data*',\n",
       " 'Datasets as Topic',\n",
       " 'Decision Making, Computer-Assisted',\n",
       " 'Decision Making, Computer-Assisted*',\n",
       " 'Decision Support Systems, Clinical',\n",
       " 'Decision Support Systems, Clinical*',\n",
       " 'Decision Support Systems, Management*',\n",
       " 'Decision Support Techniques*',\n",
       " 'Decision Trees',\n",
       " 'Delayed Diagnosis',\n",
       " 'Demography',\n",
       " 'Diabetic Ketoacidosis/blood*',\n",
       " 'Diabetic Ketoacidosis/mortality*',\n",
       " 'Diagnosis, Computer-Assisted/methods',\n",
       " 'Diagnosis, Computer-Assisted/methods*',\n",
       " 'Diagnostic Errors/prevention & control*',\n",
       " 'Disseminated Intravascular Coagulation*',\n",
       " 'Dose-Response Relationship, Drug',\n",
       " 'Early Diagnosis*',\n",
       " 'Electrocardiography',\n",
       " 'Electrocardiography*',\n",
       " 'Electrocardiography/instrumentation*',\n",
       " 'Electrocardiography/methods*',\n",
       " 'Electronic Data Processing',\n",
       " 'Electronic Health Records',\n",
       " 'Electronic Health Records*',\n",
       " 'Electronic Health Records*/classification',\n",
       " 'Electronic Health Records/classification*',\n",
       " 'Electronic Health Records/organization & administration*',\n",
       " 'Electronic Health Records/standards',\n",
       " 'Electronic Health Records/statistics & numerical data',\n",
       " 'Electronic Health Records/statistics & numerical data*',\n",
       " 'Equipment Failure*',\n",
       " 'Erythrocyte Indices*',\n",
       " 'Erythrocyte Volume*',\n",
       " 'European Continental Ancestry Group/statistics & numerical data',\n",
       " 'Evaluation Studies as Topic',\n",
       " 'Expert Systems',\n",
       " 'Expert Systems*',\n",
       " 'False Positive Reactions',\n",
       " 'Female',\n",
       " 'Fever/drug therapy*',\n",
       " 'Fever/etiology',\n",
       " 'Fluid Therapy*',\n",
       " 'Forecasting',\n",
       " 'Fourier Analysis',\n",
       " 'Fuzzy Logic',\n",
       " 'Fuzzy Logic*',\n",
       " 'Genomics',\n",
       " 'Health Information Exchange/standards*',\n",
       " 'Health Information Interoperability/standards',\n",
       " 'Health Level Seven',\n",
       " 'Health Status',\n",
       " 'Health Status Disparities*',\n",
       " 'Health Status Indicators',\n",
       " 'Health Status Indicators*',\n",
       " 'Heart Failure/diagnosis*',\n",
       " 'Heart Failure/etiology',\n",
       " 'Heart Failure/physiopathology*',\n",
       " 'Heart Function Tests/methods*',\n",
       " 'Heart Rate',\n",
       " 'Heart Rate*',\n",
       " 'Heart Rate/physiology',\n",
       " 'Heart Rate/physiology*',\n",
       " 'Heart Ventricles/pathology',\n",
       " 'Heart Ventricles/physiopathology',\n",
       " 'Heart/physiology*',\n",
       " 'Hemodynamics',\n",
       " 'Hemodynamics*',\n",
       " 'Hemodynamics/physiology*',\n",
       " 'Heparin/administration & dosage*',\n",
       " 'Heparin/pharmacology*',\n",
       " 'Hepatitis/blood*',\n",
       " 'Hepatitis/mortality',\n",
       " 'Hepatitis/physiopathology',\n",
       " 'Hospital Mortality',\n",
       " 'Hospital Mortality*',\n",
       " 'Hospital Mortality/ethnology*',\n",
       " 'Humans',\n",
       " 'Hyperglycemia/diagnosis',\n",
       " 'Hyperlactatemia*',\n",
       " 'Hypoglycemia/diagnosis',\n",
       " 'Hypotension/diagnosis*',\n",
       " 'Hypotension/physiopathology',\n",
       " 'Hypotension/physiopathology*',\n",
       " 'Hypotension/prevention & control*',\n",
       " 'Hypovolemia*',\n",
       " 'Hypoxia/blood*',\n",
       " 'Hypoxia/mortality',\n",
       " 'Hypoxia/physiopathology',\n",
       " 'Iatrogenic Disease',\n",
       " 'Incidence',\n",
       " 'Infant',\n",
       " 'Infant, Newborn',\n",
       " 'Infant, Newborn*',\n",
       " 'Information Storage and Retrieval',\n",
       " 'Information Storage and Retrieval*',\n",
       " 'Information Storage and Retrieval/methods',\n",
       " 'Information Storage and Retrieval/methods*',\n",
       " 'Information Storage and Retrieval/statistics & numerical data*',\n",
       " 'Information Systems',\n",
       " 'Information Theory*',\n",
       " 'Intensive Care Units',\n",
       " 'Intensive Care Units*',\n",
       " 'Intensive Care Units*/organization & administration',\n",
       " 'Intensive Care Units, Pediatric',\n",
       " 'Intensive Care Units/organization & administration',\n",
       " 'Intensive Care Units/organization & administration*',\n",
       " 'Intensive Care Units/standards*',\n",
       " 'Intensive Care Units/statistics & numerical data',\n",
       " 'Intensive Care Units/statistics & numerical data*',\n",
       " 'International Classification of Diseases',\n",
       " 'International Classification of Diseases*',\n",
       " 'International Normalized Ratio',\n",
       " 'Internet',\n",
       " 'Intubation',\n",
       " 'Isotonic Solutions/administration & dosage*',\n",
       " 'Jaundice, Neonatal/classification',\n",
       " 'Jaundice, Neonatal/diagnosis',\n",
       " 'Kaplan-Meier Estimate',\n",
       " 'Knowledge Bases',\n",
       " 'L-Lactate Dehydrogenase/blood',\n",
       " 'Learning*',\n",
       " 'Least-Squares Analysis',\n",
       " 'Length of Stay',\n",
       " 'Leukocyte Count*',\n",
       " 'Leukocytosis/etiology',\n",
       " 'Linear Models',\n",
       " 'Linguistics',\n",
       " 'Liver Cirrhosis/blood',\n",
       " 'Liver Cirrhosis/complications',\n",
       " 'Liver Cirrhosis/diagnosis',\n",
       " 'Liver Cirrhosis/diagnosis*',\n",
       " 'Liver Cirrhosis/mortality*',\n",
       " 'Liver Failure/blood',\n",
       " 'Liver Failure/diagnosis*',\n",
       " 'Liver Failure/etiology',\n",
       " 'Liver Failure/mortality*',\n",
       " 'Logistic Models',\n",
       " 'Longitudinal Studies',\n",
       " 'Lymphocyte Count',\n",
       " 'Lymphocytes/cytology*',\n",
       " 'Machine Learning',\n",
       " 'Machine Learning*',\n",
       " 'Machine Learning/statistics & numerical data*',\n",
       " 'Male',\n",
       " 'Markov Chains',\n",
       " 'Markov Chains*',\n",
       " 'Massachusetts',\n",
       " 'Massachusetts/epidemiology',\n",
       " 'Medical Informatics',\n",
       " 'Medical Informatics Applications',\n",
       " 'Medical Informatics Applications*',\n",
       " 'Medical Informatics*',\n",
       " 'Medical Informatics/methods',\n",
       " 'Medical Informatics/methods*',\n",
       " 'Medical Records',\n",
       " 'Medical Records Systems, Computerized',\n",
       " 'Middle Aged',\n",
       " 'Models, Biological',\n",
       " 'Models, Biological*',\n",
       " 'Models, Cardiovascular*',\n",
       " 'Models, Statistical',\n",
       " 'Models, Statistical*',\n",
       " 'Models, Theoretical',\n",
       " 'Models, Theoretical*',\n",
       " 'Monitoring, Ambulatory/methods*',\n",
       " 'Monitoring, Physiologic',\n",
       " 'Monitoring, Physiologic*',\n",
       " 'Monitoring, Physiologic/instrumentation*',\n",
       " 'Monitoring, Physiologic/methods*',\n",
       " 'Mortality',\n",
       " 'Mortality*',\n",
       " 'Mothers*',\n",
       " 'Multiple Myeloma/complications*',\n",
       " 'Multiple Myeloma/genetics',\n",
       " 'Multiple Myeloma/therapy',\n",
       " 'Multiple Organ Failure',\n",
       " 'Multiple Organ Failure/blood',\n",
       " 'Multiple Organ Failure/diagnosis*',\n",
       " 'Multiple Organ Failure/etiology',\n",
       " 'Multiple Organ Failure/mortality*',\n",
       " 'Multiple Organ Failure/physiopathology*',\n",
       " 'Multivariate Analysis',\n",
       " 'Natural Language Processing',\n",
       " 'Natural Language Processing*',\n",
       " 'Neural Networks (Computer)',\n",
       " 'Neural Networks (Computer)*',\n",
       " 'Neutrophils/cytology*',\n",
       " 'New York',\n",
       " 'New York/epidemiology',\n",
       " 'Nonlinear Dynamics',\n",
       " 'Norepinephrine/antagonists & inhibitors*',\n",
       " 'Nurses',\n",
       " 'Nursing Records*',\n",
       " 'Obesity/complications',\n",
       " 'Obesity/mortality*',\n",
       " 'Organ Dysfunction Scores',\n",
       " 'Organ Dysfunction Scores*',\n",
       " 'Outcome Assessment (Health Care)',\n",
       " 'Overweight/complications',\n",
       " 'Overweight/mortality*',\n",
       " 'Oxygen/metabolism',\n",
       " 'Pancreatitis/blood*',\n",
       " 'Pancreatitis/diagnosis*',\n",
       " 'Pancreatitis/etiology',\n",
       " 'Pancreatitis/mortality*',\n",
       " 'Pancreatitis/therapy*',\n",
       " 'Partial Pressure',\n",
       " 'Patient Admission',\n",
       " 'Patient Admission*',\n",
       " 'Patient Discharge',\n",
       " 'Patient Discharge*',\n",
       " 'Patient Readmission*',\n",
       " 'Patient Readmission/statistics & numerical data*',\n",
       " 'Patient-Specific Modeling*',\n",
       " 'Pattern Recognition, Automated',\n",
       " 'Pattern Recognition, Automated*',\n",
       " 'Pattern Recognition, Automated/methods',\n",
       " 'Pattern Recognition, Automated/methods*',\n",
       " 'Phenotype*',\n",
       " 'Photoplethysmography',\n",
       " 'Photoplethysmography/methods*',\n",
       " 'Phototherapy/statistics & numerical data',\n",
       " 'Pilot Projects',\n",
       " 'Platelet Count',\n",
       " 'Plethysmography*',\n",
       " 'Postoperative Complications/diagnosis*',\n",
       " 'Postoperative Complications/epidemiology*',\n",
       " 'Predictive Value of Tests',\n",
       " 'Pressure',\n",
       " 'Principal Component Analysis',\n",
       " 'Probability',\n",
       " 'Prognosis',\n",
       " 'Programming Languages',\n",
       " 'Proportional Hazards Models',\n",
       " 'Prospective Studies',\n",
       " 'Quality Assurance, Health Care',\n",
       " 'Quality Control',\n",
       " 'ROC Curve',\n",
       " 'Reference Values',\n",
       " 'Regression Analysis',\n",
       " 'Regression Analysis*',\n",
       " 'Reinforcement (Psychology)*',\n",
       " 'Reproducibility of Results',\n",
       " 'Research*',\n",
       " 'Respiration Disorders/diagnosis*',\n",
       " 'Respiration Disorders/mortality',\n",
       " 'Respiration Disorders/mortality*',\n",
       " 'Respiration Disorders/physiopathology*',\n",
       " 'Respiration, Artificial*',\n",
       " 'Respiration, Artificial/statistics & numerical data',\n",
       " 'Respiration, Artificial/statistics & numerical data*',\n",
       " 'Respiratory Distress Syndrome, Adult/diagnosis*',\n",
       " 'Respiratory Distress Syndrome, Adult/epidemiology*',\n",
       " 'Respiratory Distress Syndrome, Newborn/diagnosis*',\n",
       " 'Respiratory Distress Syndrome, Newborn/epidemiology',\n",
       " 'Respiratory Distress Syndrome, Newborn/rehabilitation*',\n",
       " 'Respiratory Insufficiency/diagnosis',\n",
       " 'Respiratory Insufficiency/mortality*',\n",
       " 'Respiratory Rate/physiology',\n",
       " 'Respiratory Rate/physiology*',\n",
       " 'Resuscitation*',\n",
       " 'Resuscitation/methods*',\n",
       " 'Retrospective Studies',\n",
       " \"Ringer's Lactate\",\n",
       " 'Risk',\n",
       " 'Risk Assessment',\n",
       " 'Risk Assessment*',\n",
       " 'Risk Assessment/methods',\n",
       " 'Risk Assessment/methods*',\n",
       " 'Risk Factors',\n",
       " 'Sensitivity and Specificity',\n",
       " 'Sepsis',\n",
       " 'Sepsis/complications*',\n",
       " 'Sepsis/diagnosis',\n",
       " 'Sepsis/diagnosis*',\n",
       " 'Sepsis/drug therapy*',\n",
       " 'Sepsis/epidemiology',\n",
       " 'Sepsis/mortality',\n",
       " 'Sepsis/mortality*',\n",
       " 'Sepsis/pathology*',\n",
       " 'Sepsis/physiopathology*',\n",
       " 'Sepsis/therapy*',\n",
       " 'Serotonin Uptake Inhibitors/therapeutic use*',\n",
       " 'Severity of Illness Index',\n",
       " 'Severity of Illness Index*',\n",
       " 'Shock, Septic',\n",
       " 'Shock, Septic*',\n",
       " 'Shock, Septic/mortality*',\n",
       " 'Shock, Septic/physiopathology',\n",
       " 'Signal Processing, Computer-Assisted',\n",
       " 'Signal Processing, Computer-Assisted*',\n",
       " 'Sodium Chloride/administration & dosage*',\n",
       " 'Software',\n",
       " 'Software*',\n",
       " 'Stroke Volume*',\n",
       " 'Subarachnoid Hemorrhage/blood',\n",
       " 'Subarachnoid Hemorrhage/diagnosis*',\n",
       " 'Subarachnoid Hemorrhage/physiopathology*',\n",
       " 'Subarachnoid Hemorrhage/therapy',\n",
       " 'Supervised Machine Learning/statistics & numerical data',\n",
       " 'Support Vector Machine*',\n",
       " 'Support Vector Machine/economics*',\n",
       " 'Survival Analysis',\n",
       " 'Systematized Nomenclature of Medicine',\n",
       " 'Tachycardia, Ventricular/diagnosis*',\n",
       " 'Tachycardia, Ventricular/physiopathology*',\n",
       " 'Terminology as Topic',\n",
       " 'Tertiary Care Centers',\n",
       " 'Therapeutics',\n",
       " 'Therapeutics/adverse effects*',\n",
       " 'Tilt-Table Test',\n",
       " 'Time',\n",
       " 'Time Factors',\n",
       " 'Treatment Failure',\n",
       " 'Triage/methods',\n",
       " 'Triage/methods*',\n",
       " 'Unified Medical Language System',\n",
       " 'Unified Medical Language System*',\n",
       " 'United Kingdom',\n",
       " 'United States',\n",
       " 'Unnecessary Procedures/standards*',\n",
       " 'Unsupervised Machine Learning/statistics & numerical data',\n",
       " 'User-Computer Interface',\n",
       " 'User-Computer Interface*',\n",
       " 'Vasoconstrictor Agents*/administration & dosage',\n",
       " 'Vasoconstrictor Agents*/therapeutic use',\n",
       " 'Vasoconstrictor Agents/therapeutic use',\n",
       " 'Ventricular Function, Left*',\n",
       " 'Ventricular Premature Complexes/diagnosis*',\n",
       " 'Vital Signs',\n",
       " 'Vital Signs/physiology',\n",
       " 'Wavelet Analysis',\n",
       " 'Young Adult'}"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(all_mesh_terms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1028\n",
      "432\n"
     ]
    }
   ],
   "source": [
    "print(len(all_mesh_terms))\n",
    "print(len(set(all_mesh_terms)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total mesh terms: 1028\n",
      "Unique mesh terms: 258\n"
     ]
    }
   ],
   "source": [
    "# Clean the mesh terms. Strip the * and remove things after /\n",
    "for i in range(len(all_mesh_terms)):\n",
    "    all_mesh_terms[i] = all_mesh_terms[i].strip('*').split('/')[0]\n",
    "\n",
    "print('Total mesh terms:', len(all_mesh_terms))\n",
    "print('Unique mesh terms:', len(set(all_mesh_terms)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generating figures\n",
    "mesh_text = ' '.join(term for term in all_mesh_terms)\n",
    "wordcloud = WordCloud(width=1920, height=1080, background_color=\"white\").generate(mesh_text)\n",
    "\n",
    "# Display the generated image:\n",
    "# the matplotlib way:\n",
    "plt.imshow(wordcloud, interpolation='bilinear')\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Aged',\n",
       " 'Aged, 80 and over',\n",
       " 'Algorithms',\n",
       " 'Boston',\n",
       " 'Female',\n",
       " 'Hospital Mortality',\n",
       " 'Humans',\n",
       " 'Intensive Care Units',\n",
       " 'Machine Learning',\n",
       " 'Male',\n",
       " 'Middle Aged',\n",
       " 'ROC Curve',\n",
       " 'Reproducibility of Results',\n",
       " 'Risk',\n",
       " 'Severity of Illness Index',\n",
       " 'Acute Disease',\n",
       " 'Aged',\n",
       " 'Databases, Factual',\n",
       " 'Erythrocyte Indices',\n",
       " 'Female',\n",
       " 'Humans',\n",
       " 'Intensive Care Units',\n",
       " 'Male',\n",
       " 'Middle Aged',\n",
       " 'Multivariate Analysis',\n",
       " 'Pancreatitis',\n",
       " 'Pancreatitis',\n",
       " 'Prognosis',\n",
       " 'Retrospective Studies',\n",
       " 'Academic Medical Centers',\n",
       " 'Adult',\n",
       " 'African Americans',\n",
       " 'Aged',\n",
       " 'Aged, 80 and over',\n",
       " 'Asian Americans',\n",
       " 'Boston',\n",
       " 'Continental Population Groups',\n",
       " 'Electronic Health Records',\n",
       " 'European Continental Ancestry Group',\n",
       " 'Female',\n",
       " 'Health Status Disparities',\n",
       " 'Hospital Mortality',\n",
       " 'Humans',\n",
       " 'Intensive Care Units',\n",
       " 'Male',\n",
       " 'Middle Aged',\n",
       " 'Retrospective Studies',\n",
       " 'Adult',\n",
       " 'Artificial Intelligence',\n",
       " 'Critical Care',\n",
       " 'Databases, Factual',\n",
       " 'Decision Support Systems, Clinical',\n",
       " 'Expert Systems',\n",
       " 'Female',\n",
       " 'Humans',\n",
       " 'Intensive Care Units',\n",
       " 'Medical Informatics Applications',\n",
       " 'Medical Records Systems, Computerized',\n",
       " 'Monitoring, Physiologic',\n",
       " 'Quality Control',\n",
       " 'Retrospective Studies',\n",
       " 'United States',\n",
       " 'Algorithms',\n",
       " 'Databases, Factual',\n",
       " 'Humans',\n",
       " 'Intensive Care Units',\n",
       " 'Intubation',\n",
       " 'Pressure',\n",
       " 'Respiration, Artificial',\n",
       " 'Time Factors',\n",
       " 'Access to Information',\n",
       " 'Biomedical Research',\n",
       " 'Critical Care',\n",
       " 'Databases, Factual',\n",
       " 'Humans',\n",
       " 'Internet',\n",
       " 'Software',\n",
       " 'User-Computer Interface',\n",
       " 'Aged',\n",
       " 'Critical Care',\n",
       " 'Databases, Factual',\n",
       " 'Female',\n",
       " 'Hospital Mortality',\n",
       " 'Humans',\n",
       " 'Intensive Care Units',\n",
       " 'Male',\n",
       " 'Middle Aged',\n",
       " 'Norepinephrine',\n",
       " 'Patient Admission',\n",
       " 'Retrospective Studies',\n",
       " 'Serotonin Uptake Inhibitors',\n",
       " 'Blood Pressure',\n",
       " 'Circadian Rhythm',\n",
       " 'Critical Care',\n",
       " 'Data Mining',\n",
       " 'Databases, Factual',\n",
       " 'Heart Rate',\n",
       " 'Humans',\n",
       " 'Medical Informatics',\n",
       " 'Aged',\n",
       " 'Cell Count',\n",
       " 'Erythrocyte Volume',\n",
       " 'Female',\n",
       " 'Humans',\n",
       " 'Intensive Care Units',\n",
       " 'Lymphocytes',\n",
       " 'Male',\n",
       " 'Middle Aged',\n",
       " 'Neutrophils',\n",
       " 'Patient Admission',\n",
       " 'Prognosis',\n",
       " 'Retrospective Studies',\n",
       " 'Subarachnoid Hemorrhage',\n",
       " 'Subarachnoid Hemorrhage',\n",
       " 'Subarachnoid Hemorrhage',\n",
       " 'Subarachnoid Hemorrhage',\n",
       " 'Adult',\n",
       " 'Classification',\n",
       " 'Electronic Health Records*',\n",
       " 'Humans',\n",
       " 'Infant, Newborn',\n",
       " 'Information Storage and Retrieval',\n",
       " 'Intensive Care Units',\n",
       " 'Jaundice, Neonatal',\n",
       " 'Jaundice, Neonatal',\n",
       " 'Phototherapy',\n",
       " 'Respiration, Artificial',\n",
       " 'Support Vector Machine',\n",
       " 'Aged',\n",
       " 'Calcium',\n",
       " 'Calcium',\n",
       " 'Critical Illness',\n",
       " 'Databases, Factual',\n",
       " 'Female',\n",
       " 'Humans',\n",
       " 'Intensive Care Units',\n",
       " 'Logistic Models',\n",
       " 'Male',\n",
       " 'Medical Informatics',\n",
       " 'Middle Aged',\n",
       " 'Predictive Value of Tests',\n",
       " 'Blood Pressure',\n",
       " 'Critical Care',\n",
       " 'Forecasting',\n",
       " 'Heart Rate',\n",
       " 'Humans',\n",
       " 'Intensive Care Units',\n",
       " 'Machine Learning',\n",
       " 'Multiple Organ Failure',\n",
       " 'Risk Assessment',\n",
       " 'Shock, Septic',\n",
       " 'Critical Care',\n",
       " 'Databases, Factual',\n",
       " 'Humans',\n",
       " 'Hyperlactatemia',\n",
       " 'Intensive Care Units',\n",
       " 'Sepsis',\n",
       " 'Shock, Septic',\n",
       " 'Adult',\n",
       " 'Aged',\n",
       " 'Aged, 80 and over',\n",
       " 'Databases, Factual',\n",
       " 'Demography',\n",
       " 'Female',\n",
       " 'Humans',\n",
       " 'Intensive Care Units',\n",
       " 'Linear Models',\n",
       " 'Male',\n",
       " 'Middle Aged',\n",
       " 'Models, Biological',\n",
       " 'Probability',\n",
       " 'Sepsis',\n",
       " 'Sepsis',\n",
       " 'Signal Processing, Computer-Assisted',\n",
       " 'Adult',\n",
       " 'Aged',\n",
       " 'Aged, 80 and over',\n",
       " 'Body Mass Index',\n",
       " 'Boston',\n",
       " 'Cohort Studies',\n",
       " 'Critical Illness',\n",
       " 'Female',\n",
       " 'Humans',\n",
       " 'Intensive Care Units',\n",
       " 'Male',\n",
       " 'Middle Aged',\n",
       " 'Obesity',\n",
       " 'Obesity',\n",
       " 'Overweight',\n",
       " 'Overweight',\n",
       " 'Proportional Hazards Models',\n",
       " 'Risk Factors',\n",
       " 'Severity of Illness Index',\n",
       " 'Survival Analysis',\n",
       " 'Databases, Factual',\n",
       " 'Electronic Health Records',\n",
       " 'Humans',\n",
       " 'Medical Informatics Applications',\n",
       " 'Clinical Laboratory Techniques',\n",
       " 'Databases, Factual',\n",
       " 'Humans',\n",
       " 'Information Theory',\n",
       " 'Intensive Care Units',\n",
       " 'Unnecessary Procedures',\n",
       " 'Access to Information',\n",
       " 'Adult',\n",
       " 'Critical Care',\n",
       " 'Databases, Factual',\n",
       " 'Humans',\n",
       " 'Monitoring, Physiologic',\n",
       " 'Research',\n",
       " 'Wavelet Analysis',\n",
       " 'Algorithms',\n",
       " 'Blood Pressure',\n",
       " 'Data Mining',\n",
       " 'Humans',\n",
       " 'Hypotension',\n",
       " 'Hypotension',\n",
       " 'Intensive Care Units',\n",
       " 'Pattern Recognition, Automated',\n",
       " 'Risk Factors',\n",
       " 'Algorithms',\n",
       " 'Biomarkers',\n",
       " 'Databases, Factual',\n",
       " 'Fuzzy Logic',\n",
       " 'Heart Failure',\n",
       " 'Heart Failure',\n",
       " 'Heart Failure',\n",
       " 'Hemodynamics',\n",
       " 'Humans',\n",
       " 'Intensive Care Units',\n",
       " 'Models, Theoretical',\n",
       " 'Patient Admission',\n",
       " 'Prognosis',\n",
       " 'Retrospective Studies',\n",
       " 'Severity of Illness Index',\n",
       " 'Stroke Volume',\n",
       " 'Ventricular Function, Left',\n",
       " 'Algorithms',\n",
       " 'Artifacts',\n",
       " 'Critical Care',\n",
       " 'Humans',\n",
       " 'Hypovolemia',\n",
       " 'Photoplethysmography',\n",
       " 'Signal Processing, Computer-Assisted',\n",
       " 'Boston',\n",
       " 'Critical Care',\n",
       " 'Electronic Health Records',\n",
       " 'Humans',\n",
       " 'Intensive Care Units*',\n",
       " 'Middle Aged',\n",
       " 'Prognosis',\n",
       " 'Quality Assurance, Health Care',\n",
       " 'Reproducibility of Results',\n",
       " 'Retrospective Studies',\n",
       " 'Severity of Illness Index',\n",
       " 'Data Mining',\n",
       " 'Electronic Health Records',\n",
       " 'Humans',\n",
       " 'Intensive Care Units',\n",
       " 'Medical Informatics Applications',\n",
       " 'Nurses',\n",
       " 'Regression Analysis',\n",
       " 'Risk Assessment',\n",
       " 'Acute Kidney Injury',\n",
       " 'Critical Care',\n",
       " 'Hospital Mortality',\n",
       " 'Humans',\n",
       " 'Intensive Care Units',\n",
       " 'Prognosis',\n",
       " 'ROC Curve',\n",
       " 'Sensitivity and Specificity',\n",
       " 'Support Vector Machine',\n",
       " 'Fuzzy Logic',\n",
       " 'Humans',\n",
       " 'Intensive Care Units',\n",
       " 'Monitoring, Physiologic',\n",
       " 'Risk Assessment',\n",
       " 'Algorithms',\n",
       " 'Critical Care',\n",
       " 'Critical Illness',\n",
       " 'Data Mining',\n",
       " 'Data Mining',\n",
       " 'Databases, Factual',\n",
       " 'Humans',\n",
       " 'Information Storage and Retrieval',\n",
       " 'Information Storage and Retrieval',\n",
       " 'Intensive Care Units',\n",
       " 'Models, Theoretical',\n",
       " 'Programming Languages',\n",
       " 'Reproducibility of Results',\n",
       " 'Computational Biology',\n",
       " 'Critical Care',\n",
       " 'Databases, Factual',\n",
       " 'Electronic Health Records',\n",
       " 'Female',\n",
       " 'Humans',\n",
       " 'Machine Learning',\n",
       " 'Male',\n",
       " 'Supervised Machine Learning',\n",
       " 'Unsupervised Machine Learning',\n",
       " 'Algorithms',\n",
       " 'Clinical Alarms',\n",
       " 'Critical Care',\n",
       " 'Diagnosis, Computer-Assisted',\n",
       " 'Diagnostic Errors',\n",
       " 'Electrocardiography',\n",
       " 'False Positive Reactions',\n",
       " 'Humans',\n",
       " 'Intensive Care Units',\n",
       " 'Pattern Recognition, Automated',\n",
       " 'Reproducibility of Results',\n",
       " 'Sensitivity and Specificity',\n",
       " 'Tachycardia, Ventricular',\n",
       " 'Wavelet Analysis',\n",
       " 'Artificial Intelligence',\n",
       " 'Boston',\n",
       " 'Decision Making, Computer-Assisted',\n",
       " 'Female',\n",
       " 'Fluid Therapy',\n",
       " 'Humans',\n",
       " 'Information Storage and Retrieval',\n",
       " 'Intensive Care Units',\n",
       " 'Male',\n",
       " 'Vasoconstrictor Agents',\n",
       " 'Adult',\n",
       " 'Aged',\n",
       " 'Algorithms',\n",
       " 'Computational Biology',\n",
       " 'Disseminated Intravascular Coagulation',\n",
       " 'Electronic Health Records',\n",
       " 'Female',\n",
       " 'Humans',\n",
       " 'Information Storage and Retrieval',\n",
       " 'Male',\n",
       " 'Middle Aged',\n",
       " 'Patient-Specific Modeling',\n",
       " 'Prospective Studies',\n",
       " 'Retrospective Studies',\n",
       " 'Software',\n",
       " 'Artificial Intelligence',\n",
       " 'Databases, Factual',\n",
       " 'Decision Support Systems, Clinical',\n",
       " 'Expert Systems',\n",
       " 'Humans',\n",
       " 'Information Systems',\n",
       " 'Intensive Care Units',\n",
       " 'Medical Records Systems, Computerized',\n",
       " 'Monitoring, Physiologic',\n",
       " 'Signal Processing, Computer-Assisted',\n",
       " 'Software',\n",
       " 'Acute Disease',\n",
       " 'Algorithms',\n",
       " 'Databases, Factual',\n",
       " 'Diagnosis, Computer-Assisted',\n",
       " 'Humans',\n",
       " 'Hypotension',\n",
       " 'Hypotension',\n",
       " 'Intensive Care Units',\n",
       " 'Neural Networks (Computer)',\n",
       " 'Predictive Value of Tests',\n",
       " 'Regression Analysis',\n",
       " 'Reproducibility of Results',\n",
       " 'Signal Processing, Computer-Assisted',\n",
       " 'Humans',\n",
       " 'Intensive Care Units',\n",
       " 'Logistic Models',\n",
       " 'Natural Language Processing',\n",
       " 'Resuscitation',\n",
       " 'Adult',\n",
       " 'Algorithms',\n",
       " 'Brain Injuries',\n",
       " 'Child',\n",
       " 'Critical Care',\n",
       " 'Databases, Factual',\n",
       " 'Hospital Mortality',\n",
       " 'Humans',\n",
       " 'Intensive Care Units',\n",
       " 'Intensive Care Units, Pediatric',\n",
       " 'Medical Informatics',\n",
       " 'Models, Statistical',\n",
       " 'Outcome Assessment (Health Care)',\n",
       " 'Predictive Value of Tests',\n",
       " 'Prognosis',\n",
       " 'ROC Curve',\n",
       " 'Regression Analysis',\n",
       " 'Risk Assessment',\n",
       " 'Access to Information',\n",
       " 'Biomedical Research',\n",
       " 'Comorbidity',\n",
       " 'Critical Care',\n",
       " 'Data Mining',\n",
       " 'Databases, Factual',\n",
       " 'Electronic Health Records',\n",
       " 'Humans',\n",
       " 'Organ Dysfunction Scores',\n",
       " 'ROC Curve',\n",
       " 'Reproducibility of Results',\n",
       " 'Sepsis',\n",
       " 'Severity of Illness Index',\n",
       " 'Therapeutics',\n",
       " 'Algorithms',\n",
       " 'Databases, Factual',\n",
       " 'Humans',\n",
       " 'Intensive Care Units',\n",
       " 'Acute Disease',\n",
       " 'Aged',\n",
       " 'Critical Illness',\n",
       " 'Databases, Factual',\n",
       " 'Female',\n",
       " 'Humans',\n",
       " 'Intensive Care Units',\n",
       " 'Isotonic Solutions',\n",
       " 'Logistic Models',\n",
       " 'Male',\n",
       " 'Massachusetts',\n",
       " 'Middle Aged',\n",
       " 'Pancreatitis',\n",
       " 'Pancreatitis',\n",
       " 'Pancreatitis',\n",
       " 'Resuscitation',\n",
       " 'Retrospective Studies',\n",
       " \"Ringer's Lactate\",\n",
       " 'Sodium Chloride',\n",
       " 'Adult',\n",
       " 'Aged',\n",
       " 'Area Under Curve',\n",
       " 'Cross-Sectional Studies',\n",
       " 'Female',\n",
       " 'Humans',\n",
       " 'Intensive Care Units',\n",
       " 'Machine Learning',\n",
       " 'Male',\n",
       " 'Middle Aged',\n",
       " 'Patient Readmission',\n",
       " 'ROC Curve',\n",
       " 'Retrospective Studies',\n",
       " 'Tertiary Care Centers',\n",
       " 'United Kingdom',\n",
       " 'Adult',\n",
       " 'Algorithms',\n",
       " 'Area Under Curve',\n",
       " 'Hospital Mortality',\n",
       " 'Humans',\n",
       " 'Intensive Care Units',\n",
       " 'Models, Theoretical',\n",
       " 'Natural Language Processing',\n",
       " 'Nursing Records',\n",
       " 'Risk Assessment',\n",
       " 'Severity of Illness Index',\n",
       " 'Unified Medical Language System',\n",
       " 'Adult',\n",
       " 'Aged',\n",
       " 'Critical Illness',\n",
       " 'Diabetic Ketoacidosis',\n",
       " 'Diabetic Ketoacidosis',\n",
       " 'Female',\n",
       " 'Humans',\n",
       " 'Intensive Care Units',\n",
       " 'Kaplan-Meier Estimate',\n",
       " 'Longitudinal Studies',\n",
       " 'Lymphocyte Count',\n",
       " 'Male',\n",
       " 'Middle Aged',\n",
       " 'Patient Readmission',\n",
       " 'Platelet Count',\n",
       " 'Predictive Value of Tests',\n",
       " 'Prognosis',\n",
       " 'Retrospective Studies',\n",
       " 'Time Factors',\n",
       " 'Airway Extubation',\n",
       " 'Algorithms',\n",
       " 'Artificial Intelligence',\n",
       " 'Database Management Systems',\n",
       " 'Databases, Factual',\n",
       " 'Decision Support Systems, Management',\n",
       " 'Diagnosis, Computer-Assisted',\n",
       " 'Female',\n",
       " 'Humans',\n",
       " 'Incidence',\n",
       " 'Infant, Newborn',\n",
       " 'Male',\n",
       " 'New York',\n",
       " 'Prognosis',\n",
       " 'Reproducibility of Results',\n",
       " 'Respiration, Artificial',\n",
       " 'Respiratory Distress Syndrome, Newborn',\n",
       " 'Respiratory Distress Syndrome, Newborn',\n",
       " 'Respiratory Distress Syndrome, Newborn',\n",
       " 'Risk Assessment',\n",
       " 'Sensitivity and Specificity',\n",
       " 'Treatment Failure',\n",
       " 'Critical Care',\n",
       " 'Humans',\n",
       " 'Intensive Care Units',\n",
       " 'Length of Stay',\n",
       " 'Logistic Models',\n",
       " 'Patient Readmission',\n",
       " 'Retrospective Studies',\n",
       " 'Algorithms',\n",
       " 'Blood Pressure',\n",
       " 'Heart Rate',\n",
       " 'Hemodynamics',\n",
       " 'Humans',\n",
       " 'Intensive Care Units',\n",
       " 'Monitoring, Physiologic',\n",
       " 'Multiple Organ Failure',\n",
       " 'Multiple Organ Failure',\n",
       " 'Neural Networks (Computer)',\n",
       " 'ROC Curve',\n",
       " 'Regression Analysis',\n",
       " 'Reproducibility of Results',\n",
       " 'Sensitivity and Specificity',\n",
       " 'Software',\n",
       " 'Time Factors',\n",
       " 'Humans',\n",
       " 'Hypotension',\n",
       " 'Intensive Care Units',\n",
       " 'Nonlinear Dynamics',\n",
       " 'Software',\n",
       " 'Heart Rate',\n",
       " 'Humans',\n",
       " 'Intensive Care Units',\n",
       " 'Internet',\n",
       " 'Sepsis',\n",
       " 'Sepsis',\n",
       " 'User-Computer Interface',\n",
       " 'Vital Signs',\n",
       " 'Algorithms',\n",
       " 'Decision Trees',\n",
       " 'Female',\n",
       " 'Hospital Mortality',\n",
       " 'Humans',\n",
       " 'Intensive Care Units',\n",
       " 'Logistic Models',\n",
       " 'Male',\n",
       " 'Multivariate Analysis',\n",
       " 'Respiration Disorders',\n",
       " 'Respiration, Artificial',\n",
       " 'Time Factors',\n",
       " 'Adult',\n",
       " 'Blood Pressure',\n",
       " 'Critical Care',\n",
       " 'Humans',\n",
       " 'Monitoring, Physiologic',\n",
       " 'Vasoconstrictor Agents*',\n",
       " 'Vasoconstrictor Agents*',\n",
       " 'Aged',\n",
       " 'Algorithms',\n",
       " 'Critical Care',\n",
       " 'Critical Illness',\n",
       " 'Electronic Data Processing',\n",
       " 'Female',\n",
       " 'Health Status',\n",
       " 'Humans',\n",
       " 'Intensive Care Units',\n",
       " 'Least-Squares Analysis',\n",
       " 'Linear Models',\n",
       " 'Machine Learning',\n",
       " 'Male',\n",
       " 'Medical Informatics',\n",
       " 'Middle Aged',\n",
       " 'Monitoring, Ambulatory',\n",
       " 'Principal Component Analysis',\n",
       " 'Reproducibility of Results',\n",
       " 'Sensitivity and Specificity',\n",
       " 'Signal Processing, Computer-Assisted',\n",
       " 'Vital Signs',\n",
       " 'Adult',\n",
       " 'Algorithms',\n",
       " 'Blood Pressure',\n",
       " 'Databases, Factual',\n",
       " 'Female',\n",
       " 'Health Status Indicators',\n",
       " 'Heart Rate',\n",
       " 'Hospital Mortality',\n",
       " 'Humans',\n",
       " 'Intensive Care Units',\n",
       " 'Male',\n",
       " 'Medical Informatics',\n",
       " 'Models, Statistical',\n",
       " 'Monitoring, Physiologic',\n",
       " 'Prognosis',\n",
       " 'Reproducibility of Results',\n",
       " 'Tilt-Table Test',\n",
       " 'Adult',\n",
       " 'Datasets as Topic',\n",
       " 'Decision Making, Computer-Assisted',\n",
       " 'Decision Support Systems, Clinical',\n",
       " 'Humans',\n",
       " 'Intensive Care Units',\n",
       " 'Length of Stay',\n",
       " 'Patient Discharge',\n",
       " 'Prognosis',\n",
       " 'ROC Curve',\n",
       " 'Retrospective Studies',\n",
       " 'Risk Assessment',\n",
       " 'Risk Factors',\n",
       " 'Triage',\n",
       " 'Data Mining',\n",
       " 'Databases, Factual',\n",
       " 'Humans',\n",
       " 'Intensive Care Units',\n",
       " 'Regression Analysis',\n",
       " 'Support Vector Machine',\n",
       " 'Critical Care',\n",
       " 'Databases, Factual',\n",
       " 'Humans',\n",
       " 'Medical Records',\n",
       " 'Nurses',\n",
       " 'Postoperative Complications',\n",
       " 'Postoperative Complications',\n",
       " 'Respiratory Distress Syndrome, Adult',\n",
       " 'Respiratory Distress Syndrome, Adult',\n",
       " 'Respiratory Rate',\n",
       " 'Risk',\n",
       " 'Adult',\n",
       " 'Critical Illness',\n",
       " 'Cross Infection',\n",
       " 'Electronic Health Records',\n",
       " 'Humans',\n",
       " 'Iatrogenic Disease',\n",
       " 'Intensive Care Units',\n",
       " 'International Classification of Diseases',\n",
       " 'Phenotype',\n",
       " 'Time',\n",
       " 'Critical Care',\n",
       " 'Data Interpretation, Statistical',\n",
       " 'Hemodynamics',\n",
       " 'Humans',\n",
       " 'Hypotension',\n",
       " 'Hypotension',\n",
       " 'Pattern Recognition, Automated',\n",
       " 'Regression Analysis',\n",
       " 'Acute Disease',\n",
       " 'Algorithms',\n",
       " 'Blood Pressure',\n",
       " 'Databases, Factual',\n",
       " 'Early Diagnosis',\n",
       " 'Humans',\n",
       " 'Intensive Care Units',\n",
       " 'Models, Theoretical',\n",
       " 'Sensitivity and Specificity',\n",
       " 'Anti-Bacterial Agents',\n",
       " 'Clostridium Infections',\n",
       " 'Clostridium Infections',\n",
       " 'Clostridium difficile',\n",
       " 'Critical Illness',\n",
       " 'Delayed Diagnosis',\n",
       " 'Electronic Health Records',\n",
       " 'Genomics',\n",
       " 'Humans',\n",
       " 'Intensive Care Units',\n",
       " 'International Classification of Diseases',\n",
       " 'Leukocyte Count',\n",
       " 'Leukocytosis',\n",
       " 'Medical Records Systems, Computerized',\n",
       " 'Phenotype',\n",
       " 'Reference Values',\n",
       " 'Acute Disease',\n",
       " 'Algorithms',\n",
       " 'Biomedical Engineering',\n",
       " 'Blood Pressure',\n",
       " 'Cardiology',\n",
       " 'Humans',\n",
       " 'Hypotension',\n",
       " 'Hypotension',\n",
       " 'Neural Networks (Computer)',\n",
       " 'Signal Processing, Computer-Assisted',\n",
       " 'Software',\n",
       " 'Time Factors',\n",
       " 'Adolescent',\n",
       " 'Adult',\n",
       " 'Aged',\n",
       " 'Algorithms',\n",
       " 'Child',\n",
       " 'Child, Preschool',\n",
       " 'Electrocardiography',\n",
       " 'Fourier Analysis',\n",
       " 'Humans',\n",
       " 'Infant',\n",
       " 'Intensive Care Units',\n",
       " 'Middle Aged',\n",
       " 'Plethysmography',\n",
       " 'Respiratory Rate',\n",
       " 'Signal Processing, Computer-Assisted',\n",
       " 'Young Adult',\n",
       " 'Algorithms',\n",
       " 'Critical Care',\n",
       " 'Decision Support Systems, Clinical',\n",
       " 'Diagnosis, Computer-Assisted',\n",
       " 'Hypotension',\n",
       " 'Monitoring, Physiologic',\n",
       " 'Multiple Organ Failure',\n",
       " 'New York',\n",
       " 'Prognosis',\n",
       " 'Reproducibility of Results',\n",
       " 'Sensitivity and Specificity',\n",
       " 'Algorithms',\n",
       " 'Hospital Mortality',\n",
       " 'Humans',\n",
       " 'Intensive Care Units',\n",
       " 'Markov Chains',\n",
       " 'Monitoring, Physiologic',\n",
       " 'Oxygen',\n",
       " 'Partial Pressure',\n",
       " 'Pattern Recognition, Automated',\n",
       " 'Respiration Disorders',\n",
       " 'Respiration Disorders',\n",
       " 'Respiration Disorders',\n",
       " 'Respiratory Rate',\n",
       " 'Algorithms',\n",
       " 'Databases, Factual',\n",
       " 'False Positive Reactions',\n",
       " 'Heart',\n",
       " 'Heart Function Tests',\n",
       " 'Heart Rate',\n",
       " 'Humans',\n",
       " 'Sensitivity and Specificity',\n",
       " 'Software',\n",
       " 'Adult',\n",
       " 'Aged',\n",
       " 'Aged, 80 and over',\n",
       " 'Antipyretics',\n",
       " 'Antipyretics',\n",
       " 'Body Temperature',\n",
       " 'Body Temperature',\n",
       " 'Critical Care',\n",
       " 'Critical Illness',\n",
       " 'Cryotherapy',\n",
       " 'Databases, Factual',\n",
       " 'Female',\n",
       " 'Humans',\n",
       " 'Male',\n",
       " 'Middle Aged',\n",
       " 'Multivariate Analysis',\n",
       " 'Probability',\n",
       " 'Risk Factors',\n",
       " 'Sepsis',\n",
       " 'Sepsis',\n",
       " 'Sepsis',\n",
       " 'Computer Simulation',\n",
       " 'Decision Support Techniques',\n",
       " 'Electronic Health Records',\n",
       " 'Humans',\n",
       " 'Models, Statistical',\n",
       " 'Phenotype',\n",
       " 'Algorithms',\n",
       " 'Blood Pressure Determination',\n",
       " 'Computer Simulation',\n",
       " 'Critical Care',\n",
       " 'Diagnosis, Computer-Assisted',\n",
       " 'Electrocardiography',\n",
       " 'Humans',\n",
       " 'Hypotension',\n",
       " 'Models, Cardiovascular',\n",
       " 'Reproducibility of Results',\n",
       " 'Risk Assessment',\n",
       " 'Risk Factors',\n",
       " 'Sensitivity and Specificity',\n",
       " 'Acute Kidney Injury',\n",
       " 'Acute Kidney Injury',\n",
       " 'Acute Kidney Injury',\n",
       " 'Adult',\n",
       " 'Age Factors',\n",
       " 'Aged',\n",
       " 'Alanine Transaminase',\n",
       " 'Bilirubin',\n",
       " 'Biomarkers',\n",
       " 'Critical Illness',\n",
       " 'Female',\n",
       " 'Hepatitis',\n",
       " 'Hepatitis',\n",
       " 'Hepatitis',\n",
       " 'Hospital Mortality',\n",
       " 'Humans',\n",
       " 'Hypoxia',\n",
       " 'Hypoxia',\n",
       " 'Hypoxia',\n",
       " 'L-Lactate Dehydrogenase',\n",
       " 'Male',\n",
       " 'Middle Aged',\n",
       " 'Predictive Value of Tests',\n",
       " 'Retrospective Studies',\n",
       " 'Risk Factors',\n",
       " 'Vasoconstrictor Agents',\n",
       " 'Adult',\n",
       " 'Aged',\n",
       " 'Aged, 80 and over',\n",
       " 'Critical Care',\n",
       " 'Critical Illness',\n",
       " 'Electronic Health Records',\n",
       " 'Hospital Mortality',\n",
       " 'Humans',\n",
       " 'Medical Informatics',\n",
       " 'Middle Aged',\n",
       " 'ROC Curve',\n",
       " 'Regression Analysis',\n",
       " 'Sepsis',\n",
       " 'Sepsis',\n",
       " 'Sepsis',\n",
       " 'Young Adult',\n",
       " 'Equipment Failure',\n",
       " 'Humans',\n",
       " 'Information Storage and Retrieval',\n",
       " 'Monitoring, Physiologic',\n",
       " 'Algorithms',\n",
       " 'Arrhythmias, Cardiac',\n",
       " 'Artificial Intelligence',\n",
       " 'Blood Pressure Determination',\n",
       " 'Critical Care',\n",
       " 'Diagnosis, Computer-Assisted',\n",
       " 'Electrocardiography',\n",
       " 'False Positive Reactions',\n",
       " 'Humans',\n",
       " 'Pattern Recognition, Automated',\n",
       " 'Reproducibility of Results',\n",
       " 'Sensitivity and Specificity',\n",
       " 'Aged',\n",
       " 'Aged, 80 and over',\n",
       " 'Antipyretics',\n",
       " 'Boston',\n",
       " 'Critical Illness',\n",
       " 'Female',\n",
       " 'Fever',\n",
       " 'Fever',\n",
       " 'Humans',\n",
       " 'Male',\n",
       " 'Middle Aged',\n",
       " 'Sepsis',\n",
       " 'Sepsis',\n",
       " 'Aged',\n",
       " 'Area Under Curve',\n",
       " 'Arterial Pressure',\n",
       " 'Bilirubin',\n",
       " 'Biomarkers',\n",
       " 'Creatinine',\n",
       " 'Critical Illness',\n",
       " 'Databases, Factual',\n",
       " 'Decision Support Techniques',\n",
       " 'Female',\n",
       " 'Humans',\n",
       " 'International Normalized Ratio',\n",
       " 'Kaplan-Meier Estimate',\n",
       " 'Liver Cirrhosis',\n",
       " 'Liver Cirrhosis',\n",
       " 'Liver Cirrhosis',\n",
       " 'Liver Cirrhosis',\n",
       " 'Liver Failure',\n",
       " 'Liver Failure',\n",
       " 'Liver Failure',\n",
       " 'Liver Failure',\n",
       " 'Male',\n",
       " 'Middle Aged',\n",
       " 'Models, Biological',\n",
       " 'Multiple Organ Failure',\n",
       " 'Multiple Organ Failure',\n",
       " 'Multiple Organ Failure',\n",
       " 'Multiple Organ Failure',\n",
       " 'Organ Dysfunction Scores',\n",
       " 'Predictive Value of Tests',\n",
       " 'Prognosis',\n",
       " 'ROC Curve',\n",
       " 'Reproducibility of Results',\n",
       " 'Risk Assessment',\n",
       " 'Risk Factors',\n",
       " 'Time Factors',\n",
       " 'Blood Glucose',\n",
       " 'Critical Illness',\n",
       " 'Critical Illness',\n",
       " 'Female',\n",
       " 'Humans',\n",
       " 'Hyperglycemia',\n",
       " 'Hypoglycemia',\n",
       " 'Male',\n",
       " 'Middle Aged',\n",
       " 'Organ Dysfunction Scores',\n",
       " 'Prognosis',\n",
       " 'Proportional Hazards Models',\n",
       " 'Reproducibility of Results',\n",
       " 'Severity of Illness Index',\n",
       " 'Arrhythmias, Cardiac',\n",
       " 'Arrhythmias, Cardiac',\n",
       " 'Clinical Alarms',\n",
       " 'Costs and Cost Analysis',\n",
       " 'Electrocardiography',\n",
       " 'False Positive Reactions',\n",
       " 'Heart Rate',\n",
       " 'Humans',\n",
       " 'Signal Processing, Computer-Assisted',\n",
       " 'Support Vector Machine',\n",
       " 'Time Factors',\n",
       " 'Adult',\n",
       " 'Alcoholism',\n",
       " 'Female',\n",
       " 'Humans',\n",
       " 'Male',\n",
       " 'Middle Aged',\n",
       " 'Models, Biological',\n",
       " 'Predictive Value of Tests',\n",
       " 'Software',\n",
       " 'Time Factors',\n",
       " 'Triage',\n",
       " 'Algorithms',\n",
       " 'Electronic Health Records',\n",
       " 'Humans',\n",
       " 'Models, Theoretical',\n",
       " 'Mortality',\n",
       " 'Time Factors',\n",
       " 'Algorithms',\n",
       " 'Cluster Analysis',\n",
       " 'Databases, Factual',\n",
       " 'Aged',\n",
       " 'Critical Illness',\n",
       " 'Female',\n",
       " 'Health Status Indicators',\n",
       " 'Humans',\n",
       " 'Liver Cirrhosis',\n",
       " 'Liver Cirrhosis',\n",
       " 'Male',\n",
       " 'Middle Aged',\n",
       " 'Prognosis',\n",
       " 'Respiratory Insufficiency',\n",
       " 'Respiratory Insufficiency',\n",
       " 'Retrospective Studies',\n",
       " 'Risk Assessment',\n",
       " 'Severity of Illness Index',\n",
       " 'Algorithms',\n",
       " 'Databases, Factual',\n",
       " 'Humans',\n",
       " 'Photoplethysmography',\n",
       " 'Signal Processing, Computer-Assisted',\n",
       " 'Ventricular Premature Complexes',\n",
       " 'Adult',\n",
       " 'Computational Biology',\n",
       " 'Databases, Factual',\n",
       " 'Hemodynamics',\n",
       " 'Humans',\n",
       " 'Models, Biological',\n",
       " 'Multivariate Analysis',\n",
       " 'Prognosis',\n",
       " 'Shock, Septic',\n",
       " 'Shock, Septic',\n",
       " 'Aged',\n",
       " 'Aged, 80 and over',\n",
       " 'Critical Care',\n",
       " 'Databases, Factual',\n",
       " 'Humans',\n",
       " 'Middle Aged',\n",
       " 'Mortality',\n",
       " 'Patient Discharge',\n",
       " 'Pattern Recognition, Automated',\n",
       " 'Prognosis',\n",
       " 'Algorithms',\n",
       " 'Blood Pressure',\n",
       " 'Electrocardiography',\n",
       " 'Heart Ventricles',\n",
       " 'Heart Ventricles',\n",
       " 'Hemodynamics',\n",
       " 'Humans',\n",
       " 'Monitoring, Physiologic',\n",
       " 'Tachycardia, Ventricular',\n",
       " 'Wavelet Analysis',\n",
       " 'Algorithms',\n",
       " 'Databases, Factual',\n",
       " 'Electronic Health Records',\n",
       " 'Female',\n",
       " 'Humans',\n",
       " 'Infant, Newborn',\n",
       " 'Massachusetts',\n",
       " 'Mothers',\n",
       " 'Pattern Recognition, Automated',\n",
       " 'Algorithms',\n",
       " 'Datasets as Topic',\n",
       " 'Electronic Health Records',\n",
       " 'Electronic Health Records',\n",
       " 'Health Information Exchange',\n",
       " 'Health Information Interoperability',\n",
       " 'Health Level Seven',\n",
       " 'Humans',\n",
       " 'Internet',\n",
       " 'Pilot Projects',\n",
       " 'Software',\n",
       " 'Algorithms',\n",
       " 'Databases, Factual',\n",
       " 'Dose-Response Relationship, Drug',\n",
       " 'Heparin',\n",
       " 'Heparin',\n",
       " 'Humans',\n",
       " 'Learning',\n",
       " 'Length of Stay',\n",
       " 'Markov Chains',\n",
       " 'Reinforcement (Psychology)',\n",
       " 'Retrospective Studies',\n",
       " 'Bayes Theorem',\n",
       " 'Data Mining',\n",
       " 'Humans',\n",
       " 'Knowledge Bases',\n",
       " 'Linguistics',\n",
       " 'Medical Informatics',\n",
       " ...]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_mesh_terms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Academic Medical Centers',\n",
       " 'Access to Information',\n",
       " 'Acute Disease',\n",
       " 'Acute Kidney Injury',\n",
       " 'Adolescent',\n",
       " 'Adult',\n",
       " 'African Americans',\n",
       " 'Age Factors',\n",
       " 'Aged',\n",
       " 'Aged, 80 and over',\n",
       " 'Airway Extubation',\n",
       " 'Alanine Transaminase',\n",
       " 'Alcoholism',\n",
       " 'Algorithms',\n",
       " 'Anti-Bacterial Agents',\n",
       " 'Antipyretics',\n",
       " 'Area Under Curve',\n",
       " 'Arrhythmias, Cardiac',\n",
       " 'Arterial Pressure',\n",
       " 'Artifacts',\n",
       " 'Artificial Intelligence',\n",
       " 'Asian Americans',\n",
       " 'Bayes Theorem',\n",
       " 'Bilirubin',\n",
       " 'Biomarkers',\n",
       " 'Biomedical Engineering',\n",
       " 'Biomedical Research',\n",
       " 'Blood Glucose',\n",
       " 'Blood Pressure',\n",
       " 'Blood Pressure Determination',\n",
       " 'Body Mass Index',\n",
       " 'Body Temperature',\n",
       " 'Boston',\n",
       " 'Brain Injuries',\n",
       " 'Brain Ischemia',\n",
       " 'Calcium',\n",
       " 'Cardiology',\n",
       " 'Cell Count',\n",
       " 'Child',\n",
       " 'Child, Preschool',\n",
       " 'Circadian Rhythm',\n",
       " 'Classification',\n",
       " 'Clinical Alarms',\n",
       " 'Clinical Coding',\n",
       " 'Clinical Laboratory Techniques',\n",
       " 'Clostridium Infections',\n",
       " 'Clostridium difficile',\n",
       " 'Cluster Analysis',\n",
       " 'Cohort Studies',\n",
       " 'Comorbidity',\n",
       " 'Computational Biology',\n",
       " 'Computer Simulation',\n",
       " 'Continental Population Groups',\n",
       " 'Costs and Cost Analysis',\n",
       " 'Creatinine',\n",
       " 'Critical Care',\n",
       " 'Critical Illness',\n",
       " 'Cross Infection',\n",
       " 'Cross-Sectional Studies',\n",
       " 'Cryotherapy',\n",
       " 'Data Interpretation, Statistical',\n",
       " 'Data Mining',\n",
       " 'Database Management Systems',\n",
       " 'Databases, Factual',\n",
       " 'Datasets as Topic',\n",
       " 'Decision Making, Computer-Assisted',\n",
       " 'Decision Support Systems, Clinical',\n",
       " 'Decision Support Systems, Management',\n",
       " 'Decision Support Techniques',\n",
       " 'Decision Trees',\n",
       " 'Delayed Diagnosis',\n",
       " 'Demography',\n",
       " 'Diabetic Ketoacidosis',\n",
       " 'Diagnosis, Computer-Assisted',\n",
       " 'Diagnostic Errors',\n",
       " 'Disseminated Intravascular Coagulation',\n",
       " 'Dose-Response Relationship, Drug',\n",
       " 'Early Diagnosis',\n",
       " 'Electrocardiography',\n",
       " 'Electronic Data Processing',\n",
       " 'Electronic Health Records',\n",
       " 'Electronic Health Records*',\n",
       " 'Equipment Failure',\n",
       " 'Erythrocyte Indices',\n",
       " 'Erythrocyte Volume',\n",
       " 'European Continental Ancestry Group',\n",
       " 'Evaluation Studies as Topic',\n",
       " 'Expert Systems',\n",
       " 'False Positive Reactions',\n",
       " 'Female',\n",
       " 'Fever',\n",
       " 'Fluid Therapy',\n",
       " 'Forecasting',\n",
       " 'Fourier Analysis',\n",
       " 'Fuzzy Logic',\n",
       " 'Genomics',\n",
       " 'Health Information Exchange',\n",
       " 'Health Information Interoperability',\n",
       " 'Health Level Seven',\n",
       " 'Health Status',\n",
       " 'Health Status Disparities',\n",
       " 'Health Status Indicators',\n",
       " 'Heart',\n",
       " 'Heart Failure',\n",
       " 'Heart Function Tests',\n",
       " 'Heart Rate',\n",
       " 'Heart Ventricles',\n",
       " 'Hemodynamics',\n",
       " 'Heparin',\n",
       " 'Hepatitis',\n",
       " 'Hospital Mortality',\n",
       " 'Humans',\n",
       " 'Hyperglycemia',\n",
       " 'Hyperlactatemia',\n",
       " 'Hypoglycemia',\n",
       " 'Hypotension',\n",
       " 'Hypovolemia',\n",
       " 'Hypoxia',\n",
       " 'Iatrogenic Disease',\n",
       " 'Incidence',\n",
       " 'Infant',\n",
       " 'Infant, Newborn',\n",
       " 'Information Storage and Retrieval',\n",
       " 'Information Systems',\n",
       " 'Information Theory',\n",
       " 'Intensive Care Units',\n",
       " 'Intensive Care Units*',\n",
       " 'Intensive Care Units, Pediatric',\n",
       " 'International Classification of Diseases',\n",
       " 'International Normalized Ratio',\n",
       " 'Internet',\n",
       " 'Intubation',\n",
       " 'Isotonic Solutions',\n",
       " 'Jaundice, Neonatal',\n",
       " 'Kaplan-Meier Estimate',\n",
       " 'Knowledge Bases',\n",
       " 'L-Lactate Dehydrogenase',\n",
       " 'Learning',\n",
       " 'Least-Squares Analysis',\n",
       " 'Length of Stay',\n",
       " 'Leukocyte Count',\n",
       " 'Leukocytosis',\n",
       " 'Linear Models',\n",
       " 'Linguistics',\n",
       " 'Liver Cirrhosis',\n",
       " 'Liver Failure',\n",
       " 'Logistic Models',\n",
       " 'Longitudinal Studies',\n",
       " 'Lymphocyte Count',\n",
       " 'Lymphocytes',\n",
       " 'Machine Learning',\n",
       " 'Male',\n",
       " 'Markov Chains',\n",
       " 'Massachusetts',\n",
       " 'Medical Informatics',\n",
       " 'Medical Informatics Applications',\n",
       " 'Medical Records',\n",
       " 'Medical Records Systems, Computerized',\n",
       " 'Middle Aged',\n",
       " 'Models, Biological',\n",
       " 'Models, Cardiovascular',\n",
       " 'Models, Statistical',\n",
       " 'Models, Theoretical',\n",
       " 'Monitoring, Ambulatory',\n",
       " 'Monitoring, Physiologic',\n",
       " 'Mortality',\n",
       " 'Mothers',\n",
       " 'Multiple Myeloma',\n",
       " 'Multiple Organ Failure',\n",
       " 'Multivariate Analysis',\n",
       " 'Natural Language Processing',\n",
       " 'Neural Networks (Computer)',\n",
       " 'Neutrophils',\n",
       " 'New York',\n",
       " 'Nonlinear Dynamics',\n",
       " 'Norepinephrine',\n",
       " 'Nurses',\n",
       " 'Nursing Records',\n",
       " 'Obesity',\n",
       " 'Organ Dysfunction Scores',\n",
       " 'Outcome Assessment (Health Care)',\n",
       " 'Overweight',\n",
       " 'Oxygen',\n",
       " 'Pancreatitis',\n",
       " 'Partial Pressure',\n",
       " 'Patient Admission',\n",
       " 'Patient Discharge',\n",
       " 'Patient Readmission',\n",
       " 'Patient-Specific Modeling',\n",
       " 'Pattern Recognition, Automated',\n",
       " 'Phenotype',\n",
       " 'Photoplethysmography',\n",
       " 'Phototherapy',\n",
       " 'Pilot Projects',\n",
       " 'Platelet Count',\n",
       " 'Plethysmography',\n",
       " 'Postoperative Complications',\n",
       " 'Predictive Value of Tests',\n",
       " 'Pressure',\n",
       " 'Principal Component Analysis',\n",
       " 'Probability',\n",
       " 'Prognosis',\n",
       " 'Programming Languages',\n",
       " 'Proportional Hazards Models',\n",
       " 'Prospective Studies',\n",
       " 'Quality Assurance, Health Care',\n",
       " 'Quality Control',\n",
       " 'ROC Curve',\n",
       " 'Reference Values',\n",
       " 'Regression Analysis',\n",
       " 'Reinforcement (Psychology)',\n",
       " 'Reproducibility of Results',\n",
       " 'Research',\n",
       " 'Respiration Disorders',\n",
       " 'Respiration, Artificial',\n",
       " 'Respiratory Distress Syndrome, Adult',\n",
       " 'Respiratory Distress Syndrome, Newborn',\n",
       " 'Respiratory Insufficiency',\n",
       " 'Respiratory Rate',\n",
       " 'Resuscitation',\n",
       " 'Retrospective Studies',\n",
       " \"Ringer's Lactate\",\n",
       " 'Risk',\n",
       " 'Risk Assessment',\n",
       " 'Risk Factors',\n",
       " 'Sensitivity and Specificity',\n",
       " 'Sepsis',\n",
       " 'Serotonin Uptake Inhibitors',\n",
       " 'Severity of Illness Index',\n",
       " 'Shock, Septic',\n",
       " 'Signal Processing, Computer-Assisted',\n",
       " 'Sodium Chloride',\n",
       " 'Software',\n",
       " 'Stroke Volume',\n",
       " 'Subarachnoid Hemorrhage',\n",
       " 'Supervised Machine Learning',\n",
       " 'Support Vector Machine',\n",
       " 'Survival Analysis',\n",
       " 'Systematized Nomenclature of Medicine',\n",
       " 'Tachycardia, Ventricular',\n",
       " 'Terminology as Topic',\n",
       " 'Tertiary Care Centers',\n",
       " 'Therapeutics',\n",
       " 'Tilt-Table Test',\n",
       " 'Time',\n",
       " 'Time Factors',\n",
       " 'Treatment Failure',\n",
       " 'Triage',\n",
       " 'Unified Medical Language System',\n",
       " 'United Kingdom',\n",
       " 'United States',\n",
       " 'Unnecessary Procedures',\n",
       " 'Unsupervised Machine Learning',\n",
       " 'User-Computer Interface',\n",
       " 'Vasoconstrictor Agents',\n",
       " 'Vasoconstrictor Agents*',\n",
       " 'Ventricular Function, Left',\n",
       " 'Ventricular Premature Complexes',\n",
       " 'Vital Signs',\n",
       " 'Wavelet Analysis',\n",
       " 'Young Adult'}"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(all_mesh_terms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the counts, and count percentages (divided by number of papers)\n",
    "counts = []\n",
    "\n",
    "for term in set(all_mesh_terms):\n",
    "    wc = all_mesh_terms.count(term)\n",
    "    counts.append([wc, math.ceil(wc / len(titles) * 100), term])\n",
    "    \n",
    "counts.sort(reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[85, 68, 'Humans'],\n",
       " [46, 37, 'Intensive Care Units'],\n",
       " [37, 30, 'Databases, Factual'],\n",
       " [32, 26, 'Algorithms'],\n",
       " [27, 22, 'Female'],\n",
       " [26, 21, 'Critical Care'],\n",
       " [25, 20, 'Male'],\n",
       " [24, 20, 'Middle Aged'],\n",
       " [21, 17, 'Aged'],\n",
       " [21, 17, 'Adult'],\n",
       " [17, 14, 'Prognosis'],\n",
       " [17, 14, 'Electronic Health Records'],\n",
       " [16, 13, 'Retrospective Studies'],\n",
       " [16, 13, 'Critical Illness'],\n",
       " [15, 12, 'Reproducibility of Results'],\n",
       " [14, 12, 'Sepsis'],\n",
       " [11, 9, 'Monitoring, Physiologic'],\n",
       " [11, 9, 'Hypotension'],\n",
       " [11, 9, 'Hospital Mortality'],\n",
       " [10, 8, 'Sensitivity and Specificity'],\n",
       " [10, 8, 'Risk Assessment'],\n",
       " [9, 8, 'Time Factors'],\n",
       " [9, 8, 'Software'],\n",
       " [9, 8, 'Signal Processing, Computer-Assisted'],\n",
       " [9, 8, 'ROC Curve'],\n",
       " [9, 8, 'Blood Pressure'],\n",
       " [8, 7, 'Severity of Illness Index'],\n",
       " [8, 7, 'Multiple Organ Failure'],\n",
       " [8, 7, 'Data Mining'],\n",
       " [8, 7, 'Aged, 80 and over'],\n",
       " [7, 6, 'Risk Factors'],\n",
       " [7, 6, 'Regression Analysis'],\n",
       " [7, 6, 'Predictive Value of Tests'],\n",
       " [7, 6, 'Pattern Recognition, Automated'],\n",
       " [7, 6, 'Medical Informatics'],\n",
       " [7, 6, 'Information Storage and Retrieval'],\n",
       " [7, 6, 'Heart Rate'],\n",
       " [6, 5, 'Liver Cirrhosis'],\n",
       " [6, 5, 'Electrocardiography'],\n",
       " [6, 5, 'Diagnosis, Computer-Assisted'],\n",
       " [6, 5, 'Boston'],\n",
       " [5, 4, 'Support Vector Machine'],\n",
       " [5, 4, 'Pancreatitis'],\n",
       " [5, 4, 'Models, Theoretical'],\n",
       " [5, 4, 'Machine Learning'],\n",
       " [5, 4, 'Logistic Models'],\n",
       " [5, 4, 'Hemodynamics'],\n",
       " [5, 4, 'Decision Support Systems, Clinical'],\n",
       " [5, 4, 'Artificial Intelligence'],\n",
       " [5, 4, 'Acute Disease'],\n",
       " [4, 4, 'Vasoconstrictor Agents'],\n",
       " [4, 4, 'Subarachnoid Hemorrhage'],\n",
       " [4, 4, 'Shock, Septic'],\n",
       " [4, 4, 'Respiration, Artificial'],\n",
       " [4, 4, 'Respiration Disorders'],\n",
       " [4, 4, 'Phenotype'],\n",
       " [4, 4, 'Multivariate Analysis'],\n",
       " [4, 4, 'Models, Biological'],\n",
       " [4, 4, 'Liver Failure'],\n",
       " [4, 4, 'Internet'],\n",
       " [4, 4, 'International Classification of Diseases'],\n",
       " [4, 4, 'False Positive Reactions'],\n",
       " [4, 4, 'Acute Kidney Injury'],\n",
       " [3, 3, 'Wavelet Analysis'],\n",
       " [3, 3, 'User-Computer Interface'],\n",
       " [3, 3, 'Respiratory Rate'],\n",
       " [3, 3, 'Respiratory Distress Syndrome, Newborn'],\n",
       " [3, 3, 'Patient Readmission'],\n",
       " [3, 3, 'Patient Discharge'],\n",
       " [3, 3, 'Patient Admission'],\n",
       " [3, 3, 'Organ Dysfunction Scores'],\n",
       " [3, 3, 'Neural Networks (Computer)'],\n",
       " [3, 3, 'Natural Language Processing'],\n",
       " [3, 3, 'Multiple Myeloma'],\n",
       " [3, 3, 'Models, Statistical'],\n",
       " [3, 3, 'Medical Records Systems, Computerized'],\n",
       " [3, 3, 'Medical Informatics Applications'],\n",
       " [3, 3, 'Length of Stay'],\n",
       " [3, 3, 'Infant, Newborn'],\n",
       " [3, 3, 'Hypoxia'],\n",
       " [3, 3, 'Hepatitis'],\n",
       " [3, 3, 'Heart Failure'],\n",
       " [3, 3, 'Computational Biology'],\n",
       " [3, 3, 'Biomarkers'],\n",
       " [3, 3, 'Arrhythmias, Cardiac'],\n",
       " [3, 3, 'Area Under Curve'],\n",
       " [3, 3, 'Antipyretics'],\n",
       " [3, 3, 'Access to Information'],\n",
       " [2, 2, 'Young Adult'],\n",
       " [2, 2, 'Vital Signs'],\n",
       " [2, 2, 'Unified Medical Language System'],\n",
       " [2, 2, 'Triage'],\n",
       " [2, 2, 'Therapeutics'],\n",
       " [2, 2, 'Tachycardia, Ventricular'],\n",
       " [2, 2, 'Risk'],\n",
       " [2, 2, 'Resuscitation'],\n",
       " [2, 2, 'Respiratory Insufficiency'],\n",
       " [2, 2, 'Respiratory Distress Syndrome, Adult'],\n",
       " [2, 2, 'Proportional Hazards Models'],\n",
       " [2, 2, 'Probability'],\n",
       " [2, 2, 'Postoperative Complications'],\n",
       " [2, 2, 'Pilot Projects'],\n",
       " [2, 2, 'Photoplethysmography'],\n",
       " [2, 2, 'Overweight'],\n",
       " [2, 2, 'Obesity'],\n",
       " [2, 2, 'Nurses'],\n",
       " [2, 2, 'New York'],\n",
       " [2, 2, 'Mortality'],\n",
       " [2, 2, 'Massachusetts'],\n",
       " [2, 2, 'Markov Chains'],\n",
       " [2, 2, 'Linear Models'],\n",
       " [2, 2, 'Kaplan-Meier Estimate'],\n",
       " [2, 2, 'Jaundice, Neonatal'],\n",
       " [2, 2, 'Heparin'],\n",
       " [2, 2, 'Heart Ventricles'],\n",
       " [2, 2, 'Health Status Indicators'],\n",
       " [2, 2, 'Fuzzy Logic'],\n",
       " [2, 2, 'Fever'],\n",
       " [2, 2, 'Expert Systems'],\n",
       " [2, 2, 'Diabetic Ketoacidosis'],\n",
       " [2, 2, 'Decision Support Techniques'],\n",
       " [2, 2, 'Decision Making, Computer-Assisted'],\n",
       " [2, 2, 'Datasets as Topic'],\n",
       " [2, 2, 'Computer Simulation'],\n",
       " [2, 2, 'Clostridium Infections'],\n",
       " [2, 2, 'Clinical Alarms'],\n",
       " [2, 2, 'Child'],\n",
       " [2, 2, 'Calcium'],\n",
       " [2, 2, 'Body Temperature'],\n",
       " [2, 2, 'Blood Pressure Determination'],\n",
       " [2, 2, 'Biomedical Research'],\n",
       " [2, 2, 'Bilirubin'],\n",
       " [1, 1, 'Ventricular Premature Complexes'],\n",
       " [1, 1, 'Ventricular Function, Left'],\n",
       " [1, 1, 'Unsupervised Machine Learning'],\n",
       " [1, 1, 'Unnecessary Procedures'],\n",
       " [1, 1, 'United States'],\n",
       " [1, 1, 'United Kingdom'],\n",
       " [1, 1, 'Treatment Failure'],\n",
       " [1, 1, 'Time'],\n",
       " [1, 1, 'Tilt-Table Test'],\n",
       " [1, 1, 'Tertiary Care Centers'],\n",
       " [1, 1, 'Terminology as Topic'],\n",
       " [1, 1, 'Systematized Nomenclature of Medicine'],\n",
       " [1, 1, 'Survival Analysis'],\n",
       " [1, 1, 'Supervised Machine Learning'],\n",
       " [1, 1, 'Stroke Volume'],\n",
       " [1, 1, 'Sodium Chloride'],\n",
       " [1, 1, 'Serotonin Uptake Inhibitors'],\n",
       " [1, 1, \"Ringer's Lactate\"],\n",
       " [1, 1, 'Research'],\n",
       " [1, 1, 'Reinforcement (Psychology)'],\n",
       " [1, 1, 'Reference Values'],\n",
       " [1, 1, 'Quality Control'],\n",
       " [1, 1, 'Quality Assurance, Health Care'],\n",
       " [1, 1, 'Prospective Studies'],\n",
       " [1, 1, 'Programming Languages'],\n",
       " [1, 1, 'Principal Component Analysis'],\n",
       " [1, 1, 'Pressure'],\n",
       " [1, 1, 'Plethysmography'],\n",
       " [1, 1, 'Platelet Count'],\n",
       " [1, 1, 'Phototherapy'],\n",
       " [1, 1, 'Patient-Specific Modeling'],\n",
       " [1, 1, 'Partial Pressure'],\n",
       " [1, 1, 'Oxygen'],\n",
       " [1, 1, 'Outcome Assessment (Health Care)'],\n",
       " [1, 1, 'Nursing Records'],\n",
       " [1, 1, 'Norepinephrine'],\n",
       " [1, 1, 'Nonlinear Dynamics'],\n",
       " [1, 1, 'Neutrophils'],\n",
       " [1, 1, 'Mothers'],\n",
       " [1, 1, 'Monitoring, Ambulatory'],\n",
       " [1, 1, 'Models, Cardiovascular'],\n",
       " [1, 1, 'Medical Records'],\n",
       " [1, 1, 'Lymphocytes'],\n",
       " [1, 1, 'Lymphocyte Count'],\n",
       " [1, 1, 'Longitudinal Studies'],\n",
       " [1, 1, 'Linguistics'],\n",
       " [1, 1, 'Leukocytosis'],\n",
       " [1, 1, 'Leukocyte Count'],\n",
       " [1, 1, 'Least-Squares Analysis'],\n",
       " [1, 1, 'Learning'],\n",
       " [1, 1, 'L-Lactate Dehydrogenase'],\n",
       " [1, 1, 'Knowledge Bases'],\n",
       " [1, 1, 'Isotonic Solutions'],\n",
       " [1, 1, 'Intubation'],\n",
       " [1, 1, 'International Normalized Ratio'],\n",
       " [1, 1, 'Intensive Care Units, Pediatric'],\n",
       " [1, 1, 'Information Theory'],\n",
       " [1, 1, 'Information Systems'],\n",
       " [1, 1, 'Infant'],\n",
       " [1, 1, 'Incidence'],\n",
       " [1, 1, 'Iatrogenic Disease'],\n",
       " [1, 1, 'Hypovolemia'],\n",
       " [1, 1, 'Hypoglycemia'],\n",
       " [1, 1, 'Hyperlactatemia'],\n",
       " [1, 1, 'Hyperglycemia'],\n",
       " [1, 1, 'Heart Function Tests'],\n",
       " [1, 1, 'Heart'],\n",
       " [1, 1, 'Health Status Disparities'],\n",
       " [1, 1, 'Health Status'],\n",
       " [1, 1, 'Health Level Seven'],\n",
       " [1, 1, 'Health Information Interoperability'],\n",
       " [1, 1, 'Health Information Exchange'],\n",
       " [1, 1, 'Genomics'],\n",
       " [1, 1, 'Fourier Analysis'],\n",
       " [1, 1, 'Forecasting'],\n",
       " [1, 1, 'Fluid Therapy'],\n",
       " [1, 1, 'Evaluation Studies as Topic'],\n",
       " [1, 1, 'European Continental Ancestry Group'],\n",
       " [1, 1, 'Erythrocyte Volume'],\n",
       " [1, 1, 'Erythrocyte Indices'],\n",
       " [1, 1, 'Equipment Failure'],\n",
       " [1, 1, 'Electronic Data Processing'],\n",
       " [1, 1, 'Early Diagnosis'],\n",
       " [1, 1, 'Dose-Response Relationship, Drug'],\n",
       " [1, 1, 'Disseminated Intravascular Coagulation'],\n",
       " [1, 1, 'Diagnostic Errors'],\n",
       " [1, 1, 'Demography'],\n",
       " [1, 1, 'Delayed Diagnosis'],\n",
       " [1, 1, 'Decision Trees'],\n",
       " [1, 1, 'Decision Support Systems, Management'],\n",
       " [1, 1, 'Database Management Systems'],\n",
       " [1, 1, 'Data Interpretation, Statistical'],\n",
       " [1, 1, 'Cryotherapy'],\n",
       " [1, 1, 'Cross-Sectional Studies'],\n",
       " [1, 1, 'Cross Infection'],\n",
       " [1, 1, 'Creatinine'],\n",
       " [1, 1, 'Costs and Cost Analysis'],\n",
       " [1, 1, 'Continental Population Groups'],\n",
       " [1, 1, 'Comorbidity'],\n",
       " [1, 1, 'Cohort Studies'],\n",
       " [1, 1, 'Cluster Analysis'],\n",
       " [1, 1, 'Clostridium difficile'],\n",
       " [1, 1, 'Clinical Laboratory Techniques'],\n",
       " [1, 1, 'Clinical Coding'],\n",
       " [1, 1, 'Classification'],\n",
       " [1, 1, 'Circadian Rhythm'],\n",
       " [1, 1, 'Child, Preschool'],\n",
       " [1, 1, 'Cell Count'],\n",
       " [1, 1, 'Cardiology'],\n",
       " [1, 1, 'Brain Ischemia'],\n",
       " [1, 1, 'Brain Injuries'],\n",
       " [1, 1, 'Body Mass Index'],\n",
       " [1, 1, 'Blood Glucose'],\n",
       " [1, 1, 'Biomedical Engineering'],\n",
       " [1, 1, 'Bayes Theorem'],\n",
       " [1, 1, 'Asian Americans'],\n",
       " [1, 1, 'Artifacts'],\n",
       " [1, 1, 'Arterial Pressure'],\n",
       " [1, 1, 'Anti-Bacterial Agents'],\n",
       " [1, 1, 'Alcoholism'],\n",
       " [1, 1, 'Alanine Transaminase'],\n",
       " [1, 1, 'Airway Extubation'],\n",
       " [1, 1, 'Age Factors'],\n",
       " [1, 1, 'African Americans'],\n",
       " [1, 1, 'Adolescent'],\n",
       " [1, 1, 'Academic Medical Centers']]"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write results to pipe delimited file.\n",
    "with open('/home/cx1111/Projects/citation-tracker/search_results/pubmed/mesh-counts.csv', 'w') as f:\n",
    "    f.write('term|count_percentage\\n')\n",
    "    for count, pcount, word in counts:\n",
    "        f.write('|'.join([word, str(pcount)]) + '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Organize the publication types\n",
    "pub_counts = []\n",
    "\n",
    "for term in set(all_publication_types):\n",
    "    wc = all_publication_types.count(term)\n",
    "    pub_counts.append([wc, math.ceil(wc / len(titles) * 100), term])    \n",
    "\n",
    "# There is also the unspecified category\n",
    "wc = len(titles) - len(all_publication_types)\n",
    "pub_counts.append([wc, math.ceil(wc / len(titles) * 100), 'Unspecified'])\n",
    "\n",
    "pub_counts.sort(reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[78, 63, 'Unspecified'],\n",
       " [19, 16, 'Research Support, N.I.H., Extramural'],\n",
       " [15, 12, \"Research Support, Non-U.S. Gov't\"],\n",
       " [6, 5, 'Comparative Study'],\n",
       " [3, 3, 'Research Support, N.I.H., Intramural'],\n",
       " [2, 2, \"Research Support, U.S. Gov't, Non-P.H.S.\"],\n",
       " [1, 1, 'Observational Study'],\n",
       " [1, 1, 'Evaluation Studies']]"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pub_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "country.max().sort_values(by=\"points\",ascending=False)[\"points\"].plot.bar()\n",
    "plt.xticks(rotation=50)\n",
    "plt.xlabel(\"Country of Origin\")\n",
    "plt.ylabel(\"Highest point of Wines\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Bar graph of publication types\n",
    "x = range(len(pub_counts))\n",
    "plt.figure()\n",
    "plt.bar(x, [p[1] for p in pub_counts])\n",
    "# Strip the long labels on the x axis\n",
    "plt.xticks(x, [p[2].strip('Research Support, ') for p in pub_counts])\n",
    "plt.title('Publication Types')\n",
    "plt.xlabel('Category')\n",
    "plt.ylabel('Percentage')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Bar graph of publication types, excluding unspecified\n",
    "scale_factor = sum(p[1] for p in pub_counts) /  sum(p[1] for p in pub_counts[1:])\n",
    "x = range(len(pub_counts)-1)\n",
    "plt.figure()\n",
    "plt.bar(x, [p[1]*scale_factor for p in pub_counts[1:]])\n",
    "# Strip the long labels on the x axis\n",
    "plt.xticks(x, [p[2].strip('Research Support, ') for p in pub_counts[1:]])\n",
    "plt.title('Publication Types (excluding unspecified)')\n",
    "plt.xlabel('Category')\n",
    "plt.ylabel('Percentage')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[78, 63, 'Unspecified'],\n",
       " [19, 16, 'Research Support, N.I.H., Extramural'],\n",
       " [15, 12, \"Research Support, Non-U.S. Gov't\"],\n",
       " [6, 5, 'Comparative Study'],\n",
       " [3, 3, 'Research Support, N.I.H., Intramural'],\n",
       " [2, 2, \"Research Support, U.S. Gov't, Non-P.H.S.\"],\n",
       " [1, 1, 'Observational Study'],\n",
       " [1, 1, 'Evaluation Studies']]"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pub_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write results to pipe delimited file.\n",
    "with open('/home/cx1111/Projects/citation-tracker/search_results/pubmed/publication-types.csv', 'w') as f:\n",
    "    f.write('publication_type|count_percentage|count_percentage_excluding_unspecified\\n')\n",
    "    for count, pcount, ptype in pub_counts:\n",
    "        if ptype == 'Unspecified':\n",
    "            f.write('|'.join([ptype, str(pcount), '0']) + '\\n')\n",
    "        else:\n",
    "            f.write('|'.join([ptype, str(pcount), str(math.ceil(pcount * scale_factor))]) + '\\n')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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