src/Demo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Processor ObservationLatestBmiProcessor added.\n",
      "INFO:root:Adding Patient Processor for ObservationLatestBmiProcessor\n",
      "INFO:root:Name of patient processor will be PatientbmiLatestProcessor\n",
      "INFO:root:Processor PatientbmiLatestProcessor added.\n",
      "INFO:root:Processor ObservationLatestHeightProcessor added.\n",
      "INFO:root:Adding Patient Processor for ObservationLatestHeightProcessor\n",
      "INFO:root:Name of patient processor will be PatientheightLatestProcessor\n",
      "INFO:root:Processor PatientheightLatestProcessor added.\n",
      "INFO:root:Processor ObservationLatestWeightProcessor added.\n",
      "INFO:root:Adding Patient Processor for ObservationLatestWeightProcessor\n",
      "INFO:root:Name of patient processor will be PatientweightLatestProcessor\n",
      "INFO:root:Processor PatientweightLatestProcessor added.\n"
     ]
    }
   ],
   "source": [
    "from fhir_client import FHIRClient\n",
    "import logging\n",
    "import pandas as pd\n",
    "\n",
    "logger = logging.getLogger(__name__)\n",
    "logging.basicConfig(level=logging.INFO)\n",
    "\n",
    "client = FHIRClient(service_base_url='https://r3.smarthealthit.org', logger=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Querying Patients\n",
    "There are two general ways of searching for patients with specific properties.\n",
    "\n",
    "The first one is to search by coding system:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code</th>\n",
       "      <th>display</th>\n",
       "      <th>system</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>893</th>\n",
       "      <td>183450002</td>\n",
       "      <td>Admission to burn unit</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1911</th>\n",
       "      <td>305340004</td>\n",
       "      <td>Admission to long stay hospital</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>305428000</td>\n",
       "      <td>Admission to orthopedic department</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6217</th>\n",
       "      <td>305433001</td>\n",
       "      <td>Admission to trauma surgery department</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13687</th>\n",
       "      <td>35637008</td>\n",
       "      <td>Alcohol rehabilitation</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            code                                 display  \\\n",
       "893    183450002                  Admission to burn unit   \n",
       "1911   305340004         Admission to long stay hospital   \n",
       "83     305428000      Admission to orthopedic department   \n",
       "6217   305433001  Admission to trauma surgery department   \n",
       "13687   35637008                  Alcohol rehabilitation   \n",
       "\n",
       "                       system  \n",
       "893    http://snomed.info/sct  \n",
       "1911   http://snomed.info/sct  \n",
       "83     http://snomed.info/sct  \n",
       "6217   http://snomed.info/sct  \n",
       "13687  http://snomed.info/sct  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "procedures = client.get_all_procedures()\n",
    "pd.DataFrame([prod.code['coding'][0] for prod in procedures]).drop_duplicates().sort_values(by=['display']).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Retrieved 252 patients with a total of 23145 observations'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patients_by_procedure_code = client.get_patients_by_procedure_code(\"http://snomed.info/sct\",\"73761001\")\n",
    "\"Retrieved {} patients with a total of {} observations\".format( len(patients_by_procedure_code), \n",
    "                                                               sum([len(pat.observations) for pat in patients_by_procedure_code]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The second one is by text. The searched text will be `CodeableConcept.text`, `Coding.display`, or `Identifier.type.text`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code</th>\n",
       "      <th>display</th>\n",
       "      <th>system</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>486</th>\n",
       "      <td>30473006</td>\n",
       "      <td>Abdominal pain</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>102594003</td>\n",
       "      <td>Abnormal ECG</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6800</th>\n",
       "      <td>26079004</td>\n",
       "      <td>Abnormal involuntary movement</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6275</th>\n",
       "      <td>168750009</td>\n",
       "      <td>Abnormal mammogram, unspecified</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6568</th>\n",
       "      <td>312399001</td>\n",
       "      <td>Abnormal results of thyroid function studies</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           code                                       display  \\\n",
       "486    30473006                                Abdominal pain   \n",
       "138   102594003                                  Abnormal ECG   \n",
       "6800   26079004                 Abnormal involuntary movement   \n",
       "6275  168750009               Abnormal mammogram, unspecified   \n",
       "6568  312399001  Abnormal results of thyroid function studies   \n",
       "\n",
       "                      system  \n",
       "486   http://snomed.info/sct  \n",
       "138   http://snomed.info/sct  \n",
       "6800  http://snomed.info/sct  \n",
       "6275  http://snomed.info/sct  \n",
       "6568  http://snomed.info/sct  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conditions = client.get_all_conditions()\n",
    "pd.DataFrame([cond.code['coding'][0] for cond in conditions]).drop_duplicates(subset=['display']).sort_values(by='display', ascending=True).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code</th>\n",
       "      <th>display</th>\n",
       "      <th>system</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>443165006</td>\n",
       "      <td>Pathological fracture due to osteoporosis (dis...</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>36971009</td>\n",
       "      <td>Sinusitis (disorder)</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>195662009</td>\n",
       "      <td>Acute viral pharyngitis (disorder)</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>193590000</td>\n",
       "      <td>Mature cataract</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>197927001</td>\n",
       "      <td>Recurrent urinary tract infection</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>201834006</td>\n",
       "      <td>Localized, primary osteoarthritis of the hand</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>75498004</td>\n",
       "      <td>Acute bacterial sinusitis (disorder)</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>15777000</td>\n",
       "      <td>Prediabetes</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>39848009</td>\n",
       "      <td>Whiplash injury to neck</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>87433001</td>\n",
       "      <td>Pulmonary emphysema (disorder)</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>195662009</td>\n",
       "      <td>Acute viral pharyngitis (disorder)</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>263102004</td>\n",
       "      <td>Fracture subluxation of wrist</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>65966004</td>\n",
       "      <td>Fracture of forearm</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>65363002</td>\n",
       "      <td>Otitis media</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>236077008</td>\n",
       "      <td>Protracted diarrhea</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>399211009</td>\n",
       "      <td>History of myocardial infarction (situation)</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>70704007</td>\n",
       "      <td>Sprain of wrist</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>444814009</td>\n",
       "      <td>Viral sinusitis (disorder)</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>72892002</td>\n",
       "      <td>Normal pregnancy</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>16932000</td>\n",
       "      <td>Nausea and vomiting</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>15777000</td>\n",
       "      <td>Prediabetes</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>10509002</td>\n",
       "      <td>Acute bronchitis (disorder)</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>10509002</td>\n",
       "      <td>Acute bronchitis (disorder)</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>72892002</td>\n",
       "      <td>Normal pregnancy</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>10509002</td>\n",
       "      <td>Acute bronchitis (disorder)</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>36971009</td>\n",
       "      <td>Sinusitis (disorder)</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>13645005</td>\n",
       "      <td>Chronic obstructive lung disease</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>38341003</td>\n",
       "      <td>Hypertension</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29857009</td>\n",
       "      <td>Chest pain</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>109838007</td>\n",
       "      <td>Overlapping malignant neoplasm of colon</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7341</th>\n",
       "      <td>444814009</td>\n",
       "      <td>Viral sinusitis (disorder)</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7342</th>\n",
       "      <td>84027009</td>\n",
       "      <td>Pernicious anemia</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7343</th>\n",
       "      <td>65363002</td>\n",
       "      <td>Otitis media</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7344</th>\n",
       "      <td>44054006</td>\n",
       "      <td>Diabetes</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7345</th>\n",
       "      <td>128613002</td>\n",
       "      <td>Seizure disorder</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7346</th>\n",
       "      <td>38341003</td>\n",
       "      <td>Hypertension</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7347</th>\n",
       "      <td>703151001</td>\n",
       "      <td>History of single seizure (situation)</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7348</th>\n",
       "      <td>90560007</td>\n",
       "      <td>Gout</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7349</th>\n",
       "      <td>38341003</td>\n",
       "      <td>Hypertension</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7350</th>\n",
       "      <td>192127007</td>\n",
       "      <td>Child attention deficit disorder</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7351</th>\n",
       "      <td>444814009</td>\n",
       "      <td>Viral sinusitis (disorder)</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7352</th>\n",
       "      <td>195662009</td>\n",
       "      <td>Acute viral pharyngitis (disorder)</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7353</th>\n",
       "      <td>43878008</td>\n",
       "      <td>Streptococcal sore throat (disorder)</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7354</th>\n",
       "      <td>64859006</td>\n",
       "      <td>Osteoporosis (disorder)</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7355</th>\n",
       "      <td>195662009</td>\n",
       "      <td>Acute viral pharyngitis (disorder)</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7356</th>\n",
       "      <td>428251008</td>\n",
       "      <td>History of appendectomy</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7357</th>\n",
       "      <td>39848009</td>\n",
       "      <td>Whiplash injury to neck</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7358</th>\n",
       "      <td>444814009</td>\n",
       "      <td>Viral sinusitis (disorder)</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7359</th>\n",
       "      <td>410429000</td>\n",
       "      <td>Cardiac Arrest</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7360</th>\n",
       "      <td>297242006</td>\n",
       "      <td>FH: Cardiac disorder</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7361</th>\n",
       "      <td>36971009</td>\n",
       "      <td>Sinusitis (disorder)</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7362</th>\n",
       "      <td>89765005</td>\n",
       "      <td>Tobacco dependence syndrome</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7363</th>\n",
       "      <td>15777000</td>\n",
       "      <td>Prediabetes</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7364</th>\n",
       "      <td>444814009</td>\n",
       "      <td>Viral sinusitis (disorder)</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7365</th>\n",
       "      <td>254637007</td>\n",
       "      <td>Non-small cell lung cancer (disorder)</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7366</th>\n",
       "      <td>79890006</td>\n",
       "      <td>Anorexia</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7367</th>\n",
       "      <td>195662009</td>\n",
       "      <td>Acute viral pharyngitis (disorder)</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7368</th>\n",
       "      <td>72892002</td>\n",
       "      <td>Normal pregnancy</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7369</th>\n",
       "      <td>444814009</td>\n",
       "      <td>Viral sinusitis (disorder)</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7370</th>\n",
       "      <td>162573006</td>\n",
       "      <td>Suspected lung cancer (situation)</td>\n",
       "      <td>http://snomed.info/sct</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7371 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           code                                            display  \\\n",
       "0     443165006  Pathological fracture due to osteoporosis (dis...   \n",
       "1      36971009                               Sinusitis (disorder)   \n",
       "2     195662009                 Acute viral pharyngitis (disorder)   \n",
       "3     193590000                                    Mature cataract   \n",
       "4     197927001                  Recurrent urinary tract infection   \n",
       "5     201834006      Localized, primary osteoarthritis of the hand   \n",
       "6      75498004               Acute bacterial sinusitis (disorder)   \n",
       "7      15777000                                        Prediabetes   \n",
       "8      39848009                            Whiplash injury to neck   \n",
       "9      87433001                     Pulmonary emphysema (disorder)   \n",
       "10    195662009                 Acute viral pharyngitis (disorder)   \n",
       "11    263102004                      Fracture subluxation of wrist   \n",
       "12     65966004                                Fracture of forearm   \n",
       "13     65363002                                       Otitis media   \n",
       "14    236077008                                Protracted diarrhea   \n",
       "15    399211009       History of myocardial infarction (situation)   \n",
       "16     70704007                                    Sprain of wrist   \n",
       "17    444814009                         Viral sinusitis (disorder)   \n",
       "18     72892002                                   Normal pregnancy   \n",
       "19     16932000                                Nausea and vomiting   \n",
       "20     15777000                                        Prediabetes   \n",
       "21     10509002                        Acute bronchitis (disorder)   \n",
       "22     10509002                        Acute bronchitis (disorder)   \n",
       "23     72892002                                   Normal pregnancy   \n",
       "24     10509002                        Acute bronchitis (disorder)   \n",
       "25     36971009                               Sinusitis (disorder)   \n",
       "26     13645005                   Chronic obstructive lung disease   \n",
       "27     38341003                                       Hypertension   \n",
       "28     29857009                                         Chest pain   \n",
       "29    109838007            Overlapping malignant neoplasm of colon   \n",
       "...         ...                                                ...   \n",
       "7341  444814009                         Viral sinusitis (disorder)   \n",
       "7342   84027009                                  Pernicious anemia   \n",
       "7343   65363002                                       Otitis media   \n",
       "7344   44054006                                           Diabetes   \n",
       "7345  128613002                                   Seizure disorder   \n",
       "7346   38341003                                       Hypertension   \n",
       "7347  703151001              History of single seizure (situation)   \n",
       "7348   90560007                                               Gout   \n",
       "7349   38341003                                       Hypertension   \n",
       "7350  192127007                   Child attention deficit disorder   \n",
       "7351  444814009                         Viral sinusitis (disorder)   \n",
       "7352  195662009                 Acute viral pharyngitis (disorder)   \n",
       "7353   43878008               Streptococcal sore throat (disorder)   \n",
       "7354   64859006                            Osteoporosis (disorder)   \n",
       "7355  195662009                 Acute viral pharyngitis (disorder)   \n",
       "7356  428251008                            History of appendectomy   \n",
       "7357   39848009                            Whiplash injury to neck   \n",
       "7358  444814009                         Viral sinusitis (disorder)   \n",
       "7359  410429000                                     Cardiac Arrest   \n",
       "7360  297242006                               FH: Cardiac disorder   \n",
       "7361   36971009                               Sinusitis (disorder)   \n",
       "7362   89765005                        Tobacco dependence syndrome   \n",
       "7363   15777000                                        Prediabetes   \n",
       "7364  444814009                         Viral sinusitis (disorder)   \n",
       "7365  254637007              Non-small cell lung cancer (disorder)   \n",
       "7366   79890006                                           Anorexia   \n",
       "7367  195662009                 Acute viral pharyngitis (disorder)   \n",
       "7368   72892002                                   Normal pregnancy   \n",
       "7369  444814009                         Viral sinusitis (disorder)   \n",
       "7370  162573006                  Suspected lung cancer (situation)   \n",
       "\n",
       "                      system  \n",
       "0     http://snomed.info/sct  \n",
       "1     http://snomed.info/sct  \n",
       "2     http://snomed.info/sct  \n",
       "3     http://snomed.info/sct  \n",
       "4     http://snomed.info/sct  \n",
       "5     http://snomed.info/sct  \n",
       "6     http://snomed.info/sct  \n",
       "7     http://snomed.info/sct  \n",
       "8     http://snomed.info/sct  \n",
       "9     http://snomed.info/sct  \n",
       "10    http://snomed.info/sct  \n",
       "11    http://snomed.info/sct  \n",
       "12    http://snomed.info/sct  \n",
       "13    http://snomed.info/sct  \n",
       "14    http://snomed.info/sct  \n",
       "15    http://snomed.info/sct  \n",
       "16    http://snomed.info/sct  \n",
       "17    http://snomed.info/sct  \n",
       "18    http://snomed.info/sct  \n",
       "19    http://snomed.info/sct  \n",
       "20    http://snomed.info/sct  \n",
       "21    http://snomed.info/sct  \n",
       "22    http://snomed.info/sct  \n",
       "23    http://snomed.info/sct  \n",
       "24    http://snomed.info/sct  \n",
       "25    http://snomed.info/sct  \n",
       "26    http://snomed.info/sct  \n",
       "27    http://snomed.info/sct  \n",
       "28    http://snomed.info/sct  \n",
       "29    http://snomed.info/sct  \n",
       "...                      ...  \n",
       "7341  http://snomed.info/sct  \n",
       "7342  http://snomed.info/sct  \n",
       "7343  http://snomed.info/sct  \n",
       "7344  http://snomed.info/sct  \n",
       "7345  http://snomed.info/sct  \n",
       "7346  http://snomed.info/sct  \n",
       "7347  http://snomed.info/sct  \n",
       "7348  http://snomed.info/sct  \n",
       "7349  http://snomed.info/sct  \n",
       "7350  http://snomed.info/sct  \n",
       "7351  http://snomed.info/sct  \n",
       "7352  http://snomed.info/sct  \n",
       "7353  http://snomed.info/sct  \n",
       "7354  http://snomed.info/sct  \n",
       "7355  http://snomed.info/sct  \n",
       "7356  http://snomed.info/sct  \n",
       "7357  http://snomed.info/sct  \n",
       "7358  http://snomed.info/sct  \n",
       "7359  http://snomed.info/sct  \n",
       "7360  http://snomed.info/sct  \n",
       "7361  http://snomed.info/sct  \n",
       "7362  http://snomed.info/sct  \n",
       "7363  http://snomed.info/sct  \n",
       "7364  http://snomed.info/sct  \n",
       "7365  http://snomed.info/sct  \n",
       "7366  http://snomed.info/sct  \n",
       "7367  http://snomed.info/sct  \n",
       "7368  http://snomed.info/sct  \n",
       "7369  http://snomed.info/sct  \n",
       "7370  http://snomed.info/sct  \n",
       "\n",
       "[7371 rows x 3 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame([cond.code['coding'][0] for cond in conditions])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Retrieved 4 patients with a total of 581 observations'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patients_by_condition_text = client.get_patients_by_condition_text(\"Abdominal pain\")\n",
    "\"Retrieved {} patients with a total of {} observations\".format( len(patients_by_condition_text), \n",
    "                                                               sum([len(pat.observations) for pat in patients_by_condition_text]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One can also load a control group for a specific cohort of patients. The control group is of equal size of the case cohort (min size: 10) and is composed of randomly sampled patients that do not match the original query. Their class is contained in the `.case` property of the Patient object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Loaded 1628 patients IDs.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Retrieved 14 patients with a total of 836 observations\n",
      "4 are cases and 10 are controls\n"
     ]
    }
   ],
   "source": [
    "patients_by_condition_text_with_controls = client.get_patients_by_condition_text(\"Abdominal pain\", controls=True)\n",
    "print(\"Retrieved {} patients with a total of {} observations\".format( len(patients_by_condition_text_with_controls), \n",
    "                                                               sum([len(pat.observations) for pat in patients_by_condition_text_with_controls])))\n",
    "print(\"{} are cases and {} are controls\".format(len([d for d in patients_by_condition_text_with_controls if d.case]), \n",
    "                                                len([d for d in patients_by_condition_text_with_controls if not d.case])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Machine Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Extracting attributes from data set\n",
      "INFO:root:Preprocessing data\n",
      "INFO:root:Started training of clf\n",
      "INFO:root:Training completed\n",
      "INFO:root:Accuracy : 1.0, F1-score : 1.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction accuracy 1.0\n"
     ]
    }
   ],
   "source": [
    "from ml_on_fhir import MLOnFHIRClassifier\n",
    "from fhir_objects.patient import Patient\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "ml_fhir = MLOnFHIRClassifier(Patient, feature_attrs=['birthDate'],\n",
    "                             label_attrs=['gender'], preprocessor=client.preprocessor)\n",
    "X, y, trained_clf = ml_fhir.fit(patients_by_condition_text, DecisionTreeClassifier())\n",
    "\n",
    "from sklearn.metrics import accuracy_score, roc_curve, auc\n",
    "fpr, tpr, _ = roc_curve(y, trained_clf.predict(X))\n",
    "print(\"Prediction accuracy {}\".format( auc(fpr, tpr) ) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a more realistic setup with cases and controls."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Extracting attributes from data set\n",
      "INFO:root:Preprocessing data\n",
      "INFO:root:Started training of clf\n",
      "INFO:root:Training completed\n",
      "INFO:root:Accuracy : 1.0, F1-score : 1.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction accuracy 1.0\n"
     ]
    }
   ],
   "source": [
    "ml_fhir = MLOnFHIRClassifier(Patient, feature_attrs=['birthDate', 'gender'],\n",
    "                             label_attrs=['case'], preprocessor=client.preprocessor)\n",
    "X, y, trained_clf = ml_fhir.fit(patients_by_condition_text_with_controls, DecisionTreeClassifier())\n",
    "\n",
    "from sklearn.metrics import accuracy_score, roc_curve, auc\n",
    "fpr, tpr, _ = roc_curve(y, trained_clf.predict(X))\n",
    "print(\"Prediction accuracy {}\".format( auc(fpr, tpr) ) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
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     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import graphviz\n",
    "from sklearn import tree\n",
    "dot_data = tree.export_graphviz(ml_fhir.clf, out_file=None,\n",
    "                               feature_names=['age', 'gender'],\n",
    "                               class_names={0:'controls', 1:'cases'})\n",
    "graph = graphviz.Source(dot_data)\n",
    "graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Custom Preprocessing Classes\n",
    "\n",
    "#### The first five values of the ` birthDate` feature that has been preprocessed into an age in years:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[19,  0],\n",
       "       [64,  1],\n",
       "       [53,  1],\n",
       "       [55,  0],\n",
       "       [16,  0]])"
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    "X[:5]"
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   "source": [
    "If you want to preprocess a patient differently, feel free to implement your own preprocessing class. \n",
    "We can use the `register_patient_preprocessor` function to do so. It is crucial to stick to the following naming scheme: \n",
    "    \n",
    "    \"Patient<FHIR_Resource_Name>Processor\"\n",
    "    \n",
    "Furthemore, at least the methods `fit` and `transform` have to be implemented."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Processor ObservationLatestBmiProcessor added.\n",
      "INFO:root:Adding Patient Processor for ObservationLatestBmiProcessor\n",
      "INFO:root:Name of patient processor will be PatientbmiLatestProcessor\n",
      "INFO:root:Processor PatientbmiLatestProcessor added.\n",
      "INFO:root:Processor ObservationLatestHeightProcessor added.\n",
      "INFO:root:Adding Patient Processor for ObservationLatestHeightProcessor\n",
      "INFO:root:Name of patient processor will be PatientheightLatestProcessor\n",
      "INFO:root:Processor PatientheightLatestProcessor added.\n",
      "INFO:root:Processor ObservationLatestWeightProcessor added.\n",
      "INFO:root:Adding Patient Processor for ObservationLatestWeightProcessor\n",
      "INFO:root:Name of patient processor will be PatientweightLatestProcessor\n",
      "INFO:root:Processor PatientweightLatestProcessor added.\n",
      "INFO:root:Processor PatientbirthDateProcessor added.\n"
     ]
    }
   ],
   "source": [
    "from preprocessing import Preprocessing\n",
    "\n",
    "from sklearn.base import BaseEstimator\n",
    "import datetime as dt\n",
    "import numpy as np\n",
    "from fhir_objects.fhir_resources import date_format\n",
    "\n",
    "class PatientbirthDateProcessor(BaseEstimator):\n",
    "    \"\"\"\n",
    "    Calculates the age in days to use birthdate as a feature \n",
    "    \"\"\"\n",
    "    def transform(self, X, **transform_params):\n",
    "        ages = []\n",
    "        for birthdate in X:\n",
    "            b_date = dt.datetime.strptime(birthdate[0], date_format)\n",
    "            ages.append([int(\n",
    "                            (dt.datetime.now().date() - b_date.date()).days)])\n",
    "        return np.array(ages)\n",
    "\n",
    "    def fit(self, X, y=None, **fit_params):\n",
    "        return self\n",
    "    \n",
    "preprocessor = Preprocessing()\n",
    "preprocessor.register_patient_preprocessor(PatientbirthDateProcessor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Extracting attributes from data set\n",
      "INFO:root:Preprocessing data\n",
      "INFO:root:Started training of clf\n",
      "INFO:root:Training completed\n",
      "INFO:root:Accuracy : 1.0, F1-score : 1.0\n"
     ]
    }
   ],
   "source": [
    "ml_fhir = MLOnFHIRClassifier(Patient, feature_attrs=['birthDate'], label_attrs=['gender'], preprocessor=preprocessor)\n",
    "X, y, trained_clf = ml_fhir.fit(patients_by_condition_text, DecisionTreeClassifier())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The `birthDate` feature is now the age in days:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 7074],\n",
       "       [23619],\n",
       "       [19661],\n",
       "       [20179]])"
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     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "X[:5]"
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