notebooks/medical-text-data-modelling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Medical Text Data Modelling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Author:**\n",
    "- Carlos Salgado - [email](mailto:csalgado@uwo.ca) | [GitHub](https://github.com/socd06) | [LinkedIn](www.linkedin.com/in/eng-socd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem Statement\n",
    "Bob <sup>MD</sup> and Alice <sup>MD</sup> are physicians running their respective medical practices and both have a database of private medical transcriptions. You own a Natural Language Processing (NLP) company and have been contacted by these physicians because both Bob <sup>MD</sup> and Alice <sup>MD</sup> have heard of the high quality of the Machine Learning as a Service (MLaaS) solutions you provide and want you to create a text classifier to help them automatically assign a medical specialty to each new patient text transcription."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Limitations\n",
    "\n",
    "Healthcare data is highly regulated and should be, for most intents and purposes, private. Therefore, if in a medical setting, the Machine Learning model being trained should not actually look at the data. \n",
    "\n",
    "Combining both Bob's and Alice's datasets, you should be able to create a bigger, better dataset that you could use to train your model with higher accuracy, only that you can't because it's all sensitive and private data, which is why you will need [PySyft](https://github.com/OpenMined/pysyft/) and [SyferText](https://github.com/OpenMined/SyferText/) to complete the job at hand."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Exploration and Feature Engineering\n",
    "We will take advantage of this simulated dataset, since it is not a real-life scenario, we can and will be able to do data exploration to determine the best way to tackle the problem. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Verifying requirements and installing if missing. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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    },
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     ]
    }
   ],
   "source": [
    "!pip install -r ../requirements.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Exploration\n",
    "Assuming you have access to the the anonymized combined data, you can do a very high-level data exploration in order to design the text classifier for the job. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reading dataset\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "df = pd.read_csv('../data/mtsamples.csv', delimiter=',')\n",
    "df.dataframeName = 'mtsamples.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>Unnamed: 0</th>\n",
       "      <th>description</th>\n",
       "      <th>medical_specialty</th>\n",
       "      <th>sample_name</th>\n",
       "      <th>transcription</th>\n",
       "      <th>keywords</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>A 23-year-old white female presents with comp...</td>\n",
       "      <td>Allergy / Immunology</td>\n",
       "      <td>Allergic Rhinitis</td>\n",
       "      <td>SUBJECTIVE:,  This 23-year-old white female pr...</td>\n",
       "      <td>allergy / immunology, allergic rhinitis, aller...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Consult for laparoscopic gastric bypass.</td>\n",
       "      <td>Bariatrics</td>\n",
       "      <td>Laparoscopic Gastric Bypass Consult - 2</td>\n",
       "      <td>PAST MEDICAL HISTORY:, He has difficulty climb...</td>\n",
       "      <td>bariatrics, laparoscopic gastric bypass, weigh...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Consult for laparoscopic gastric bypass.</td>\n",
       "      <td>Bariatrics</td>\n",
       "      <td>Laparoscopic Gastric Bypass Consult - 1</td>\n",
       "      <td>HISTORY OF PRESENT ILLNESS: , I have seen ABC ...</td>\n",
       "      <td>bariatrics, laparoscopic gastric bypass, heart...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2-D M-Mode. Doppler.</td>\n",
       "      <td>Cardiovascular / Pulmonary</td>\n",
       "      <td>2-D Echocardiogram - 1</td>\n",
       "      <td>2-D M-MODE: , ,1.  Left atrial enlargement wit...</td>\n",
       "      <td>cardiovascular / pulmonary, 2-d m-mode, dopple...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2-D Echocardiogram</td>\n",
       "      <td>Cardiovascular / Pulmonary</td>\n",
       "      <td>2-D Echocardiogram - 2</td>\n",
       "      <td>1.  The left ventricular cavity size and wall ...</td>\n",
       "      <td>cardiovascular / pulmonary, 2-d, doppler, echo...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                                        description  \\\n",
       "0           0   A 23-year-old white female presents with comp...   \n",
       "1           1           Consult for laparoscopic gastric bypass.   \n",
       "2           2           Consult for laparoscopic gastric bypass.   \n",
       "3           3                             2-D M-Mode. Doppler.     \n",
       "4           4                                 2-D Echocardiogram   \n",
       "\n",
       "             medical_specialty                                sample_name  \\\n",
       "0         Allergy / Immunology                         Allergic Rhinitis    \n",
       "1                   Bariatrics   Laparoscopic Gastric Bypass Consult - 2    \n",
       "2                   Bariatrics   Laparoscopic Gastric Bypass Consult - 1    \n",
       "3   Cardiovascular / Pulmonary                    2-D Echocardiogram - 1    \n",
       "4   Cardiovascular / Pulmonary                    2-D Echocardiogram - 2    \n",
       "\n",
       "                                       transcription  \\\n",
       "0  SUBJECTIVE:,  This 23-year-old white female pr...   \n",
       "1  PAST MEDICAL HISTORY:, He has difficulty climb...   \n",
       "2  HISTORY OF PRESENT ILLNESS: , I have seen ABC ...   \n",
       "3  2-D M-MODE: , ,1.  Left atrial enlargement wit...   \n",
       "4  1.  The left ventricular cavity size and wall ...   \n",
       "\n",
       "                                            keywords  \n",
       "0  allergy / immunology, allergic rhinitis, aller...  \n",
       "1  bariatrics, laparoscopic gastric bypass, weigh...  \n",
       "2  bariatrics, laparoscopic gastric bypass, heart...  \n",
       "3  cardiovascular / pulmonary, 2-d m-mode, dopple...  \n",
       "4  cardiovascular / pulmonary, 2-d, doppler, echo...  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Taking a quick look at what the data looks like\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dealing with Missing Data\n",
    "We want to use the `transcription` column as part of our training data so we need to check if the column (or any other one) has blanks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0              0\n",
       "description             0\n",
       "medical_specialty       0\n",
       "sample_name             0\n",
       "transcription          33\n",
       "keywords             1068\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum(axis = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have confirmed there are 33 blanks in our column of interest but there is information in every other column except for the `keywords` column. To fix the issue, we will fill the transcription blanks with the `description` column which should have some keywords in it, allowing us to still train without removing these datapoints."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['transcription'].fillna(df['description'], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can start our exploration, beginning with listing how many different specialties there are"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# And how many different ones\n",
    "len(df['medical_specialty'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and what each specialty frequencies is"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " Surgery                          1103\n",
       " Consult - History and Phy.        516\n",
       " Cardiovascular / Pulmonary        372\n",
       " Orthopedic                        355\n",
       " Radiology                         273\n",
       " General Medicine                  259\n",
       " Gastroenterology                  230\n",
       " Neurology                         223\n",
       " SOAP / Chart / Progress Notes     166\n",
       " Obstetrics / Gynecology           160\n",
       " Urology                           158\n",
       " Discharge Summary                 108\n",
       " ENT - Otolaryngology               98\n",
       " Neurosurgery                       94\n",
       " Hematology - Oncology              90\n",
       " Ophthalmology                      83\n",
       " Nephrology                         81\n",
       " Emergency Room Reports             75\n",
       " Pediatrics - Neonatal              70\n",
       " Pain Management                    62\n",
       " Psychiatry / Psychology            53\n",
       " Office Notes                       51\n",
       " Podiatry                           47\n",
       " Dermatology                        29\n",
       " Cosmetic / Plastic Surgery         27\n",
       " Dentistry                          27\n",
       " Letters                            23\n",
       " Physical Medicine - Rehab          21\n",
       " Sleep Medicine                     20\n",
       " Endocrinology                      19\n",
       " Bariatrics                         18\n",
       " IME-QME-Work Comp etc.             16\n",
       " Chiropractic                       14\n",
       " Diets and Nutritions               10\n",
       " Rheumatology                       10\n",
       " Speech - Language                   9\n",
       " Autopsy                             8\n",
       " Lab Medicine - Pathology            8\n",
       " Allergy / Immunology                7\n",
       " Hospice - Palliative Care           6\n",
       "Name: medical_specialty, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['medical_specialty'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Visualization and Normality Testing\n",
    "Next, we will use `matplotlib` and `seaborn` to plot and visualize the dataset. We will also calculate the `skewness` and `kurtosis` of our dataset to determine how normal our distribution is.\n",
    "\n",
    "**Reference:**\n",
    "- [GoodData.com: Normality Testing - Skewness and Kurtosis\n",
    "](https://help.gooddata.com/doc/en/reporting-and-dashboards/maql-analytical-query-language/maql-expression-reference/aggregation-functions/statistical-functions/predictive-statistical-use-cases/normality-testing-skewness-and-kurtosis#:~:text=As%20a%20general%20rule%20of,the%20distribution%20is%20approximately%20symmetric.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "\n",
    "sb.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the distribution \n",
    "sb.distplot(df['medical_specialty'].value_counts());"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have a non-normal distribution, now we will double check testing by skewness and kurtosis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we define a normality testing function that uses skewness and kurtosis\n",
    "def normality_testing(sk, kt):\n",
    "    sk = round(sk,3)\n",
    "    kt = round(kt,3)\n",
    "    if kt > 0:\n",
    "        print(\"positive kurtosis =\",kt) \n",
    "    else:\n",
    "        print(\"negative kurtosis (less normal) = \",kt) \n",
    "    \n",
    "    if abs(sk) > 1:\n",
    "        print(\"skewness =\",sk,\"\\nThe distribution is highly skewed\")\n",
    "    elif abs(sk >= 0.5):\n",
    "        print(\"skewness =\",sk,\"\\nThe distribution is moderately skewed\")\n",
    "    else:\n",
    "        print(\"skewness =\",sk,\"\\nThe distribution is approximately symmetric\")\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "positive kurtosis = 15.336\n",
      "skewness = 3.516 \n",
      "The distribution is highly skewed\n"
     ]
    }
   ],
   "source": [
    "skewness = df['medical_specialty'].value_counts().skew()\n",
    "kurtosis = df['medical_specialty'].value_counts().kurt()\n",
    "\n",
    "normality_testing(skewness,kurtosis)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And a highly skewed distribution with positive kurtosis. \n",
    "Next, we will plot to visualize the value counts in a more helpful"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1c860f2410>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['medical_specialty'].value_counts()[:40].plot(kind='barh', figsize=(15,25), fontsize=16)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display the top specialties most frequently repeated in the text\n",
    "fig, ax = plt.subplots(figsize = (12,6)) # set size of figure\n",
    "specialty = df['medical_specialty'].value_counts()\n",
    "specialty = specialty[:8,]\n",
    "g = sb.barplot(specialty.index, specialty.values, alpha=0.8)\n",
    "\n",
    "plt.title(\"Top 8 Specialties in Medical Transcriptions\")\n",
    "plt.ylabel(\"Frequency\", fontsize=12)\n",
    "plt.xlabel(\"Top 8 Specialties out of 40 total\", fontsize=14)\n",
    "\n",
    "g.set_xticklabels(g.get_xticklabels(), rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We clearly need to work on our dataset since it is heavyly skewed towards `Surgery` and it won't be a very useful as training data with such a distribution. \n",
    "\n",
    "## Feature Engineering\n",
    "We will first clean up the medical specialty column of first character whitespaces."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>Unnamed: 0</th>\n",
       "      <th>description</th>\n",
       "      <th>medical_specialty</th>\n",
       "      <th>sample_name</th>\n",
       "      <th>transcription</th>\n",
       "      <th>keywords</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>A 23-year-old white female presents with comp...</td>\n",
       "      <td>Allergy / Immunology</td>\n",
       "      <td>Allergic Rhinitis</td>\n",
       "      <td>SUBJECTIVE:,  This 23-year-old white female pr...</td>\n",
       "      <td>allergy / immunology, allergic rhinitis, aller...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Consult for laparoscopic gastric bypass.</td>\n",
       "      <td>Bariatrics</td>\n",
       "      <td>Laparoscopic Gastric Bypass Consult - 2</td>\n",
       "      <td>PAST MEDICAL HISTORY:, He has difficulty climb...</td>\n",
       "      <td>bariatrics, laparoscopic gastric bypass, weigh...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Consult for laparoscopic gastric bypass.</td>\n",
       "      <td>Bariatrics</td>\n",
       "      <td>Laparoscopic Gastric Bypass Consult - 1</td>\n",
       "      <td>HISTORY OF PRESENT ILLNESS: , I have seen ABC ...</td>\n",
       "      <td>bariatrics, laparoscopic gastric bypass, heart...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2-D M-Mode. Doppler.</td>\n",
       "      <td>Cardiovascular / Pulmonary</td>\n",
       "      <td>2-D Echocardiogram - 1</td>\n",
       "      <td>2-D M-MODE: , ,1.  Left atrial enlargement wit...</td>\n",
       "      <td>cardiovascular / pulmonary, 2-d m-mode, dopple...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2-D Echocardiogram</td>\n",
       "      <td>Cardiovascular / Pulmonary</td>\n",
       "      <td>2-D Echocardiogram - 2</td>\n",
       "      <td>1.  The left ventricular cavity size and wall ...</td>\n",
       "      <td>cardiovascular / pulmonary, 2-d, doppler, echo...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                                        description  \\\n",
       "0           0   A 23-year-old white female presents with comp...   \n",
       "1           1           Consult for laparoscopic gastric bypass.   \n",
       "2           2           Consult for laparoscopic gastric bypass.   \n",
       "3           3                             2-D M-Mode. Doppler.     \n",
       "4           4                                 2-D Echocardiogram   \n",
       "\n",
       "            medical_specialty                                sample_name  \\\n",
       "0        Allergy / Immunology                         Allergic Rhinitis    \n",
       "1                  Bariatrics   Laparoscopic Gastric Bypass Consult - 2    \n",
       "2                  Bariatrics   Laparoscopic Gastric Bypass Consult - 1    \n",
       "3  Cardiovascular / Pulmonary                    2-D Echocardiogram - 1    \n",
       "4  Cardiovascular / Pulmonary                    2-D Echocardiogram - 2    \n",
       "\n",
       "                                       transcription  \\\n",
       "0  SUBJECTIVE:,  This 23-year-old white female pr...   \n",
       "1  PAST MEDICAL HISTORY:, He has difficulty climb...   \n",
       "2  HISTORY OF PRESENT ILLNESS: , I have seen ABC ...   \n",
       "3  2-D M-MODE: , ,1.  Left atrial enlargement wit...   \n",
       "4  1.  The left ventricular cavity size and wall ...   \n",
       "\n",
       "                                            keywords  \n",
       "0  allergy / immunology, allergic rhinitis, aller...  \n",
       "1  bariatrics, laparoscopic gastric bypass, weigh...  \n",
       "2  bariatrics, laparoscopic gastric bypass, heart...  \n",
       "3  cardiovascular / pulmonary, 2-d m-mode, dopple...  \n",
       "4  cardiovascular / pulmonary, 2-d, doppler, echo...  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature = 'medical_specialty'\n",
    "df[feature] = df[feature].str.replace(r'\\A ', '')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dimensionality Reduction\n",
    "\n",
    "We now know we have too many possible labels. A good data set will have 4 or 5 labels and a lot more data points so we need to reduce the number of classes. Luckily, we can group our classes using the [ultimate list of medical specialties](https://www.sgu.edu/blog/medical/ultimate-list-of-medical-specialties/) as a guide to reduce the number of classes. The downside is we will need to do some reading before we reconfigure our features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Renaming some specialties to reduce categories, starting in the same order as the list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reduced medical_specialty to 31 total classes\n"
     ]
    }
   ],
   "source": [
    "# renaming specialties under Anesthesiology and Internal Medicine\n",
    "new_class = 'Internal Medicine'\n",
    "# Grouping all anesthesiology specialties\n",
    "df[feature].mask(df[feature] == 'Hospice - Palliative Care', new_class, inplace=True)\n",
    "df[feature].mask(df[feature] == 'Pain Management', new_class, inplace=True)\n",
    "df[feature].mask(df[feature] == 'Sleep Medicine', new_class, inplace=True)\n",
    "df[feature].mask(df[feature] == 'Endocrinology', new_class, inplace=True)\n",
    "df[feature].mask(df[feature] == 'Gastroenterology', new_class, inplace=True)\n",
    "df[feature].mask(df[feature] == 'Hematology - Oncology', new_class, inplace=True)\n",
    "df[feature].mask(df[feature] == 'Nephrology', new_class, inplace=True)\n",
    "df[feature].mask(df[feature] == 'Rheumatology', new_class, inplace=True)\n",
    "df[feature].mask(df[feature] == 'Cardiovascular / Pulmonary', new_class, inplace=True)\n",
    "# General medicine is also known as Internal Medicine\n",
    "df[feature].mask(df[feature] == 'General Medicine', new_class, inplace=True)\n",
    "\n",
    "print(\"Reduced\",feature,\"to\",len(df[feature].unique()),\"total classes\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reduced medical_specialty to 26 total classes\n"
     ]
    }
   ],
   "source": [
    "new_class = 'Surgery'\n",
    "# Grouping all surgery specialties\n",
    "df[feature].mask(df[feature] == 'Surgery', new_class, inplace=True)\n",
    "df[feature].mask(df[feature] == 'Cosmetic / Plastic Surgery', new_class, inplace=True)\n",
    "df[feature].mask(df[feature] == 'Neurosurgery', new_class, inplace=True)\n",
    "df[feature].mask(df[feature] == 'ENT - Otolaryngology', new_class, inplace=True)\n",
    "df[feature].mask(df[feature] == 'Obstetrics / Gynecology', new_class, inplace=True)\n",
    "df[feature].mask(df[feature] == 'Urology', new_class, inplace=True)\n",
    "\n",
    "print(\"Reduced\",feature,\"to\",len(df[feature].unique()),\"total classes\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reduced medical_specialty to 19 total classes\n"
     ]
    }
   ],
   "source": [
    "new_class = 'Medical Records'\n",
    "# Grouping all documents\n",
    "df[feature].mask(df[feature] == 'Consult - History and Phy.', new_class, inplace=True)\n",
    "df[feature].mask(df[feature] == 'Discharge Summary', new_class, inplace=True)\n",
    "df[feature].mask(df[feature] == 'Emergency Room Reports', new_class, inplace=True)\n",
    "df[feature].mask(df[feature] == 'IME-QME-Work Comp etc.', new_class, inplace=True)\n",
    "df[feature].mask(df[feature] == 'Letters', new_class, inplace=True)\n",
    "df[feature].mask(df[feature] == 'Office Notes', new_class, inplace=True)\n",
    "df[feature].mask(df[feature] == 'SOAP / Chart / Progress Notes', new_class, inplace=True)\n",
    "df[feature].mask(df[feature] == 'Radiology', new_class, inplace=True)\n",
    "\n",
    "print(\"Reduced\",feature,\"to\",len(df[feature].unique()),\"total classes\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reduced medical_specialty to 4 total classes\n"
     ]
    }
   ],
   "source": [
    "new_class = 'Other' \n",
    "# Grouping less popular specialties and specialties with the least data points\n",
    "df[feature].mask(df[feature] == 'Diets and Nutritions', new_class, inplace=True)\n",
    "df[feature].mask(df[feature] == 'Bariatrics', new_class, inplace=True)\n",
    "df[feature].mask(df[feature] == 'Dentistry', new_class, inplace=True)\n",
    "df[feature].mask(df[feature] == 'Ophthalmology', new_class, inplace=True)\n",
    "df[feature].mask(df[feature] == 'Pediatrics - Neonatal', new_class, inplace=True)\n",
    "df[feature].mask(df[feature] == 'Dermatology', new_class, inplace=True)\n",
    "df[feature].mask(df[feature] == 'Allergy / Immunology', new_class, inplace=True)\n",
    "df[feature].mask(df[feature] == 'Speech - Language', new_class, inplace=True)\n",
    "df[feature].mask(df[feature] == 'Psychiatry / Psychology', new_class, inplace=True)\n",
    "df[feature].mask(df[feature] == 'Autopsy', new_class, inplace=True)\n",
    "df[feature].mask(df[feature] == 'Lab Medicine - Pathology', new_class, inplace=True)\n",
    "df[feature].mask(df[feature] == 'Physical Medicine - Rehab', new_class, inplace=True)\n",
    "df[feature].mask(df[feature] == 'Orthopedic', new_class, inplace=True)\n",
    "df[feature].mask(df[feature] == 'Chiropractic', new_class, inplace=True)\n",
    "df[feature].mask(df[feature] == 'Podiatry', new_class, inplace=True)\n",
    "df[feature].mask(df[feature] == 'Neurology', new_class, inplace=True)\n",
    "\n",
    "print(\"Reduced\",feature,\"to\",len(df[feature].unique()),\"total classes\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Other', 'Internal Medicine', 'Surgery', 'Medical Records'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Double checking what the classes are\n",
    "df['medical_specialty'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display the consolidated 4 specialties most frequently repeated in the text\n",
    "fig, ax = plt.subplots(figsize = (14,8)) # set size of figure\n",
    "specialty = df['medical_specialty'].value_counts()\n",
    "specialty = specialty[::,]\n",
    "g = sb.barplot(specialty.index, specialty.values, alpha=0.8)\n",
    "\n",
    "# Calculate total classes\n",
    "total = str(len(df['medical_specialty'].unique()))\n",
    "plt.title(\"Top Specialties in Medical Transcriptions\")\n",
    "plt.ylabel(\"Frequency\", fontsize=12)\n",
    "\n",
    "x_label_str = \"Top Specialties out of \" + total +\" total\"\n",
    "plt.xlabel(x_label_str, fontsize=14)\n",
    "\n",
    "g.set_xticklabels(g.get_xticklabels(), rotation=0)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KSrfa9u3bx7x58xg5ciSrV6/u9muKvqXiYiP/TjvN0CAPZk8aaOlyekX4AC+iQn04W3CRjNMXLF2OEKro1nWaK1euZNGiRSQmJrJx40ZWrFjBunXr2iyzadMm8vLySEtLo6qqiqSkJGJiYggODu60LSQkhBdeeIHU1FSam5u7/Zqi7zAqCm9tOYFRUbhvzgi0Nng/Sk+NHupDY3Mrx7IrcHLUMWKQt6VLEuK6dHmkUl5eTlZWFgkJCQAkJCSQlZVFRUVFm+W2bt1KcnIyWq0Wb29v4uLiSE1N7bJt4MCBREREYGfXPt86e57oO3YfLuBEbiW/mD4MP09nS5fTqzQaDRNH+DPA35VDJ8s4V3jR0iUJcV26PFIpKirC398fne7SSVOdToefnx9FRUV4e3u3WS4wMND0WK/XU1xc3GVbV+vuyfOu5OPTe6PZ+vraznkAa6m1uLyO9V+dZVy4H/Omh5mu9lIq6nFz/e+QJlf+bEn29nZd1tKTWmffNJjN+87xzdEiPN2dGBjQ/XHCulNTR8z5vrq4OOLrrd49Rtayz3aXLdWrZq22e5tyN5WX1/bK1TW+vm6UldWYfT1qsJZaFUXhxQ+/B+AXtw7lwoX/TmxV39RqGpfK3GNUXYuWltZOa7meWmOj9Gw/mE/q/hxmRIfg282jtq5q6oi539f6+ibKDOrci2Mt+2x32VK9P65Vq9Vc15fxLru/9Ho9JSUlGP6zcxgMBkpLS9Hr9e2WKywsND0uKioiICCgy7au1t2T5wnbsPdIESdyK/n5T4bi42EdRyKW5GCvI25CME4Odnzx3XmqZJwwYYO6DBUfHx8iIiLYvHkzAJs3byYiIqJN1xfArFmzSElJwWg0UlFRwc6dO4mPj++yrTM9fZ6wfhUXG/lo1xmGD/Bk6pjArp9wg3B2tGNGdDA6rYad6eepbWixdElCXJNuXVL87LPP8t577xEfH897773HqlWrAFi6dClHjx4FIDExkeDgYGbOnMnPf/5zli1bRkhISJdthw4dYurUqbzzzjt8+OGHTJ06lb1793b5PGHb3t9xGoNB4e7Zw9H2sbvmr5ebiwNxE4JpNRjZkZ4v44QJm6JR+vhdV3JOpT1L1/rdqTLWbjhK8rRQZk+++j0pdU2tpJ8oAazrnEpXc5eoWWtpZQM70vNx7+dA/MQQHOyvPsKANc+n0k+l0aUtvc9eK1uqt9fPqQihpvrGVt7fcYoQP1fTcPDi6vy8/jtO2M5D52lplXHChPWTUBG96tM9Z6mubebu2cOx08nu15Ug335MHRNI+cVGdh0+LwNQCqsnf9Wi15wtqGb34QKmjw8223ztfdEAfzduHqWnpKKBLzMKMRglWIT1klARvcJgNLJu+yk83Rz56dQhli7H5gwOdCdmpD+FF+okWIRVk1ARveKL7wrIL63lF9OH2fTUwJY0LNiTyZH+FJT9J1ikK0xYIQkVYXaVNU1s2HuOUUN8GB/ua+lybFpYiASLsG4SKsLsPvjiDEajwh0zw/rcTI6WEBbiSUykPwUX6vjiuwKammVaYmE9JFSEWR07V86hk6UkxAy84UYgNqdhIZ7cHBVASWU9b3x2jCaZ715YCQkVYTYtrQbe23Eaf28XZt0gE2/1piGBHtwyJpDzpbWkHcynvlHuvBeWJ6EizCb1QB6llQ0snhGGvZ3sauYwwN+Ne+eOoKa+mW3f5lItg1AKC5O/dGEWZVUNbN6fy4ThfkQOltkMzSl8gBczJw7AYFTYdiCP0sp6S5ckbmASKsIsPth5Bq1Gw8Jbh1q6lBtCfw8nZk8egKO9jrT082QXyQySwjIkVITqMn+4wPc/XGDulEF4u8s8Kb3FzcWB2ZMH0N/Dib2ZRWScLqOPjxcrrJCEilBVc4uB93ecRu/jIgNGWoCTgx0zokMYGuzB0XMVfJlRKANRil4loSJUte1AHheqG1k8I0wGjLQQnVZDTKQ/0cP9OF9ay5b9uVTWyAl80Tvkr16oprSqgS37c5kY4UfEIDk5b0kajYaIQV7MiA6hucXA1v25nC2otnRZ4gYgoSJU88GO0+h0GhbcOszSpYj/CPBxYc6UQfT3dOLro8XsO1JEc6vcKCnMR0JFqOL7MxfIPFtO4pTBeLk5WroccQVnRztmTAghKtSH7MKLbP46Vy47FmYjoSKuW3OLgf/beenkfNyEYEuXI65Cq9UwZlh/4icNAGD7gXz2H5UBKYX6JFTEdduyP5cL1Y3cOTNcTs5bOT8vZ+ZMGURosAeHT5Wx6Rs5ahHqkv8A4rqUVNaz7UAuk0f4M3ygl6XLEd1gb6flppEBzI0dgsFgJPVAPgeySmiWQSmFCiRURI8pisL7O05jp9Pyc7lz3uaE+Lsx9+bBDB/oyem8Kj7bm83Zgmq5YVJcFwkV0WOHT1/g2LkKkmKH4OkqJ+dtkb2dlokR/twWMxBXZ3u+PlrM9oP5lFc3Wro0YaMkVESPNDa38sEXpwn2dWX6+CBLlyOuk89/xg6bHOnPxbpmtuzP5ZujxTQ0yXD64trIZOGiRz7/OoeKi008uHgkOq18N+kLNBoNYSGeDApw48jZck7mVpJTfJHIwd6MGOQt0xeIbpFQEdfsfFktO9LziY3SMzTYw9LlCJU52OuYMNyPsBBPDp8uI/OHck7nVzE6tD9Dgz3QamVKaNExCRVxTYyKwr+3n8LZ0Y7kn8jJ+b7MvZ8D08YGUVbZwKFTZXybVUJWTgVjwnwZ6O+KRiPhItqT41lxTb4+WsSZ89UkTwvF1dne0uWIXuDr5cysSSH8ZFwQWq2GPd8XsnV/LoUX6uRKMdGOHKmIbqupbyZl91mGBnswJUpv6XJEL9JoNIT4uRLk24/swot8f+YCOw+dJ8DbhbFh/fH1dLZ0icJKSKiIbvto1w80NLXyy/hwtNL1cUPSajSEBnkwSO/G6fxqjp4tZ9u3eYT4uTI2rL9cWi4kVET3ZOVU8M2xYhJuGkiQr6ulyxEWptNqiRjoxdAgD07kVnL8XAWb9uUwJMidMcP6089JukZvVBIqokstrQbWbT+Fn5czCTGDLF2OsCL2dlqiQn0IC/Hg6NkKTuVVkVNUw4hBXowc4kM/R/kXc6ORE/WiS5cGHWzgzvhwHOx1li5HWCEnBzuiI/xIih3MAH9Xjp6r4Ll30tl1+DwGo4yEfCORUBGdyi+tZdu3ucRE+hMpszmKLri62BM7OpDbYwai93HhvbTTrHw7nWPnyi1dmuglEiqiQwajkXe2nsDFyY6F02U2R9F9Ph5OPDI/iv+ZN4rWViMvfpzJy58cobSqwdKlCTOTUBEd2pF+npziGu6YEYabi4OlyxE2RqPRMC7Ml+eXTiJ5Wigncit55s0DbNhzTobZ78O6FSrZ2dksWLCA+Ph4FixYQE5OTrtlDAYDq1atIi4ujhkzZpCSknLdba+88goxMTEkJiaSmJjIqlWrrmNTxbUoqazns73nGDO0P9HD/SxdjrBhdjotsycP5I/3T2ZCuC+bvsnhf986wLFs6RLri7p1acbKlStZtGgRiYmJbNy4kRUrVrBu3bo2y2zatIm8vDzS0tKoqqoiKSmJmJgYgoODe9wGkJSUxJNPPqn+losOGRWFf207iU6n4c74cBmOQ6jCy82R++dGEjs6kHXbT/HiR5lMjPDjF3FhePSTI+G+ossjlfLycrKyskhISAAgISGBrKwsKioq2iy3detWkpOT0Wq1eHt7ExcXR2pq6nW1CcvYfbiAk3lVLLh1GF5ucjObUFfEQC+eu2ciiTcP5vDpMp5581v2HyuWIV/6iC5DpaioCH9/f3S6S5eS6nQ6/Pz8KCoqardcYGCg6bFer6e4uPi62gC2bNnCnDlzuOeee8jIyOjJNoprUFJRT8ruHxg1xIdYGYpFmIm9nZbEmwfz7JKJBPi48ObmLF765AiVNU2WLk1cJ6u+M2nhwoU8+OCD2Nvb8/XXX/Pwww+zdetWvLy6Pxe6j0/v3f3t6+vWa+u6Xler1WBU+MuH32Nvr+PxxePx8bDceE5KRT1urk6mx1f+bEn29nZd1tLbtXanpo6Ys1YXF0d8vV06XcbX142/hvuzed851m09wYq3D/LgvChuGRvUrtvVlv6+wLbqVbPWLkNFr9dTUlKCwWBAp9NhMBgoLS1Fr9e3W66wsJCoqCig7RFIT9t8fX1Nrz9lyhT0ej1nzpxh4sSJ3d7A8vJajEbzH1b7+rpRVlZj9vWooaNaUw/kcSKngvsSIjA2t1p0e+qbWqmpvTSlrZurk+lnS2tpae20FkvU2lVNHTF3rfX1TZQZuneV100RfoT6u/LPLVn89f3v+Oq7fO6KDzeNhG1Lf19gW/X+uFatVnNdX8a77P7y8fEhIiKCzZs3A7B582YiIiLw9m57I9ysWbNISUnBaDRSUVHBzp07iY+Pv662kpIS0+ufOHGCgoICBg8e3OONFR07X1rLp3vOMXZYf2IiAyxdjrgB+Xu78Ps7xvOzW4aQcbqMFW8d4HhORddPFFalW91fzz77LMuXL+e1117D3d2d1atXA7B06VIeffRRRo0aRWJiIpmZmcycOROAZcuWERISAtDjthdffJHjx4+j1Wqxt7dnzZo1bY5ehDpaWg28sek4Lk52/HLWcLnaS1iMVqvh9phBjBriwxufH+evH37PrIkDuP9nUZYuTXSTRunjl1xI91d7P671/3aeZueh8/zm56MZNcTHgpX9V11TK+knLh2pWlP31+gwXzJPl3XYbolau6qpI+auNTrC/7oGlGxqMfDRrh/4MqOA0GAP7rs9An+vzs/RWAtb/n9g9u4v0bcdPVfOzkPniRsfbDWBIgSAo72Ou+LD+Z95oygpr2fVO+kcyCrp+onCoiRUbmDVdc28teUEQf37MX9aqKXLEeKqxoX58tLj0wj2deWNz4/zr9STtLTKMC/WSkLlBmU0Kvzj8+M0NLXywNxIGdJeWDU/Lxd+t2gst00eyFffF/LCuu8oqay3dFniKiRUblCbvsnhRG4ld8wII9hPZnIU1s9Op2X+tFB+NT+K8ouNPPduOt+dKrV0WeJHJFRuQJmny/h8XzYxkQFy17ywOaOH9mflkmgCvPuxdsMxPtp1hlaDTARmLSRUbjCVNU38v//7jgAfF+6SwSKFjerv4czyO8Zx67ggth/M5/99kEFVrQzxYg0kVG4gLa1G1m44SmNTKw8njcTRQc6jCNtlb6dl8cxwls4ZQU5JDaveSed0fpWly7rhSajcIBRF4d9ppzhXeJHf/GIcQb5yHkX0DTGRATxz1wScHHT85YMMdqTny4jHFiShcoPYnVHAviNFJNw0iJuiArt+ghA2JNjXlf/9ZTRRoT588MUZ3vj8OI3NrZYu64YkoXIDOJFTwQc7zzA61IekWBk7TfRNLk52LJs3ip/dMoT0k6W8sO47iivksuPeJqHSxxVcqOPVDcfw93Zh6ZxItHJiXvRhWs2lscMeWzCG6rpmnns3ncM9GMJG9JyESh9WXdfMSymZONhp+XVyFC5OVj19jhCqiRzkzcq7o9H7uPDqp0dJ2f0DBqNcdtwbJFT6qKYWAy9/ksnF+mYenR9FfwtOuCWEJfh4OLH8jvFMGxvEtgN5/PXD76mua7Z0WX2ehEof1GowsvbTo+QU1/DAnEgG690tXZIQFmFvp+Wu+HDuvT2Cc4UXefadg3LZsZlJqPQxl8f0OpZdwd2zhjM2TOafEWLKKD1P3zUBJ3sda/4vg63f5mKUy47NQkKlD1EUhXdTT3LoVBkLbh1K7Gi5dFiIy0L8XFlxdzTjwn355MuzvPzJEWobWixdVp8jodJHGBWF99JOs+9IEXNuGkT8xAGWLkkIq+PsaMdDiZEsihvG8ewKVr4t3WFqk1DpA4yKwr+2nWR3RgGzJw2Qe1GE6IRGoyFuQghP3zUee52W1f93mE1fZ/fKDLE3AgkVG2c0Kry95QR7/3O3/PxpoTJIpBDdMCjAnZVLopkY4c+Gvdms+SCD8mrrmLbalkmo2LDmFgP/32fH+OZYMUmxg5k3dYgEihDXwNnRjvvnjODe2yPILalhxdsHOXhCpiy+HnI3nI2qbWjh5fVHOHu+moXThzEzOsTSJQlhkzQaDVNG6U+JGw4AABUOSURBVBkW7MGbm7J4feNxDp8uY/HMcFyd7S1dns2RULFBF6oa+FtKJmVVjTyYNJLo4X6WLkkIm+fn5cLyxePYsj+XTV/ncCqvil/OHs6Yof0tXZpNke4vG3Mip4Ln/nWI6tpmHl8wWgJFCBXptFrmThnM//5yAm4uDrz8yRH+sek4F+vlTvzukiMVG6EoCmnp+Xy8+wcCffrxPz8bhb+Xi6XLEqJPGuDvxoq7J7D5mxy27M/l2LkKfjF9GJMj/eW8ZRckVGxAXWML/0o9xaGTpYwP8+We2yNwdpSPTghzstNpSYodwoThfry77SRvbs5i39EiFs0II6h/P0uXZ7XkP5OVO51fxZubjlNV28z8aaHMmjRAhq8XohcF+7ry1OLxfPl9AZ9+dY5n3z5I3IRg5k4ZLF/urkLeESvV3GLg869z2HYgF19PZ566c7wMDCmEhWi1Gm4dF8yE4X6s//Is2w/ms/9YMYmxQ5g6Wo9OK6enL5NQsUInciv5V+pJSisbuDlKzy+mD5NvREJYAXcXB5bcFsG0sUF89MUZ/r39FDsP5TNvaijjwvrL+RYkVKxKZU0Tn351lq+PFePr6cTjC8cQOcjb0mUJIX5ksN6dJ+8Yx/dnLvDxl2dZu+EoA/3dSIodTFSoj6XLsygJFSvQ1GJg+4E8th7IxWhUmD15AHOnDMbRXmfp0oQQHdBoNIwN8yVqqA/fHi/h86+zeemTIwwMcGPhzHCGBbih1d54Ry4SKhbU1GLgq4wCth3Io7qumQnD/Zg/LRQ/T5mlUQhbodNqmTJKz6QR/nxzrJht3+ayet0h/LycmRkdQkxkwA3VfX3jbKkVqW1oYU9mIWkH87hY30LEQC8e/ulIhgV7Wro0IUQP2em0TB0dyM2j9PxQUstHaSd5L+00n3x5lptGBjBtbBDBvq6WLtPsJFR6UX5pLV98d55vjxfT3GokcrA3c6cMkjARog/RajVMiQpkWIAr54ousuu7AvZkFrLrcAGDAtxMRzV9dVwxCRUzu1jfzIGsEr4+WkReSS32dlpiIgOIGx9MsF/f/9YixI1Ko9EQGuhBaKAHC6YP5cDxEvYdLeL9Haf58IszRAzyIjrcj7Fhvn0qYCRUzKDiYiMZZy7w3alSTudXY1QUBvq78Yu4YcREBvSpHUgI0TV3FwdmRIcwIzqEvJIaDpwoIf1EKe9sO8m7qScJDfJgdKgPo4b4EOznatM3OEuoqKC+sYX0rGL2ZxZyPKeCwgt1AOh9XJg9eQCTRvjfEH2pQoiuDfB3Y4C/G/NvCSW3pIaM0xc4crac9V+dY/1X53B1tmf4AE/CB3gxNMiDYL9+NnVzpYTKNWppNXC+rI7ckhpyimo4W1BN4YU6FMDeTktYiCc3j9IzeqgPeh8ZH0gIcXUajYZBAe4MCnDnp1OHUFnTRFZOBSdzKzmRV8mhU2UAONhrGRTgzgB/Vwb6uxHi54rexwV7O+u85aBboZKdnc3y5cupqqrC09OT1atXM2jQoDbLGAwGnn/+efbu3YtGo+H+++8nOTnZbG3mVl7dSGllPWXVjZRU1lN0oZ6i8jrKqhoxKpfmsnZxtCM0yIOJEX5MGKmnfz97q/2ghRDWzcvNkSmj9EwZpUdRFMqrGzlbeJGzBdVkF11kT2YhzS1GADQa8PVwRu/jgp+XC35ezvh6OuPj7oi3u5NFL2Hu1ppXrlzJokWLSExMZOPGjaxYsYJ169a1WWbTpk3k5eWRlpZGVVUVSUlJxMTEEBwcbJY2czpy9gJ/TzlieqzTavD3diHYz5WJEf4M8HdlgL8b/T2cTMMy+Pq6UVZWY9a6hBA3Bo1GQ39PZ/p7OjNphD8ARqNCSWU9+aW1FF6oo7C8nuLyOk7kVZrC5jJXZ3seXzCGgQFuvV57l6FSXl5OVlYW77zzDgAJCQn84Q9/oKKiAm/v/w4hsnXrVpKTk9FqtXh7exMXF0dqair33XefWdq6qyd3tIYP8OZXyVG4Odvj4+6Eh6sD2m70adrS3bPWXqudTouL06ULGpwd7TC0WsfFDVfWdTWWqLWrmjpi7lrtdFpV9zNr32d/TO16tVoNQb6uBP3o/KyiKNQ2tFB+sZGqmmaqaptobDbQ39Op2zVcudz11t1lqBQVFeHv749Od6lbR6fT4efnR1FRUZtQKSoqIjAw0PRYr9dTXFxstrbu8vLq2XmN4ECPa36Oj4/tnIy3hVqD9df+GfSGIcFeli6hHWusSW22sM9eqTfr7Q8Muo7nq1mr7VxSIIQQwup1GSp6vZ6SkhIMBgNw6eR5aWkper2+3XKFhYWmx0VFRQQEBJitTQghhPXpMlR8fHyIiIhg8+bNAGzevJmIiIg2XV8As2bNIiUlBaPRSEVFBTt37iQ+Pt5sbUIIIaxPt67+evbZZ1m+fDmvvfYa7u7urF69GoClS5fy6KOPMmrUKBITE8nMzGTmzJkALFu2jJCQEACztAkhhLA+GkX5z00XQgghxHWSE/VCCCFUI6EihBBCNRIqQgghVCOhIoQQQjUySnE33XrrrTg4OODo6AjAE088QWxsLN9//z0rVqygqamJoKAg/vKXv+Dj4wPQaZu5nD9/nmXLlpke19TUUFtby8GDBzvcht6sdfXq1Wzfvp2CggI2bdpEWFgY0PmgpT1tM0etlZWV/O53vyMvLw8HBwcGDhzIc889Z7rEPjw8nLCwMNOwPmvWrCE8PByAXbt2sWbNGgwGA5GRkfzpT3/C2dnZbLVCx/stdP6Zm3t/uFq9ne2717Mt16uzz7yn76G56u2o1urqalasWEFZWRl2dnaMGjWKlStX4uTkxPnz55k5cybDhg0zvc67776Ll9elURo+/vhj3nzzTRRFYerUqTzzzDOdD1uliG75yU9+opw6darN7wwGgxIXF6ekp6criqIoa9euVZYvX95lW296/vnnlVWrVimKcvVtUJTerTU9PV0pLCxsV8udd96pfPbZZ4qiKMpnn32m3HnnndfdZo5aKysrlW+//da0zJ///Gfl97//velxWFiYUltb2+61amtrlZtuuknJzs5WFEVRnnrqKeWVV14xa62K0rPPvDf2h47qvdKV+25Pt0UNHX3mPX0PzVlvR7Xm5+crx48fN63/V7/6lfLqq68qiqIo+fn5ysSJE6/6enl5eUpsbKxSXl6uGAwG5Z577lE2bNjQaQ3S/XUdjh07hqOjIxMmTABg4cKFpKamdtnWW5qbm9m0aRM/+9nPOl2uN2udMGFCu9EYLg9ampCQAFwatDQrK4uKiooet5mrVk9PTyZNmmR6PGbMmDajPnRkz549jBw50nQUtXDhQrZt26ZKnR3V2hlL77td1dvdfRfMX29Hn3lP30Nz1ttRrcHBwYwYMQIArVZLVFRUt/bb7du3ExcXh7e3N1qtluTkZLZu3drpc6T76xo88cQTKIrC+PHjeeyxx9oNeOnt7Y3RaKSqqqrTNk9Pz16pd9euXfj7+xMZGdnhNri7u1u81s4GLVUUpUdtPx7xwRyMRiMffPABt956a5vf33nnnRgMBqZOncojjzyCg4NDu/c4MDCQoqIis9cI1/6ZW3p/gKvvuz3ZFrXrvfIz7+l72Fv1drR/NjY2sn79eh577DHT7+rq6pg3bx4At912G/feey8ajaZH+60cqXTT+++/z+eff8769etRFIXnnnvO0iV1af369W2+6dniNlizP/zhD7i4uLB48WLT77788ks+/fRT3n//fX744QfWrl1rwQpt9zP/8b4L1rEtV/vMrdXVam1tbeU3v/kNkydPZvr06QD4+fnx1Vdf8emnn/Lmm2+SlpbGJ5980uP1Sqh00+VDdQcHBxYtWsThw4fbDXhZUVGBVqvF09Oz07beUFJSQnp6OnPmzOl0Gy7/3pK1djZoaU/bzG316tXk5uby97//vc1Jy8vrdnV1JTk5ucP3uLCwsFfq7Mlnbun94Wr77uWawXL7748/856+h71R79X2T4PBwBNPPIGHhwfPPPOMaVkHBwfTRQI+Pj7MmTPnuvZbCZVuqK+vp6bm0qyOiqKwdetWIiIiGDlyJI2NjRw6dAiADz/8kFmzZgF02tYbNmzYwC233GK6gqOjbbCGWjsbtLSnbeb04osvcuzYMdauXYuDg4Pp99XV1TQ2NgKXvhFu377d9B7HxsZy9OhRcnJygEvv8ezZs81aZ08/c0vvDz/ed69nW9Rytc+8p++hueu9Wq1Go5Hly5ej0+l44YUXTDPWwqVzmi0tLQA0NDSwa9cuhg8fDkB8fDw7d+6koqICo9FISkpKl/utjP3VDfn5+TzyyCMYDAaMRiOhoaE888wz+Pn5cfjwYVauXNnm0sD+/fsDdNpmbvHx8Tz99NNMnTq1y23ozVqff/550tLSuHDhAl5eXnh6erJlyxbOnj3L8uXLuXjxomnQ0iFDhgD0uM0ctf79738nISGBQYMG4eTkBEBwcDBr164lIyODFStWoNFoaG1tZezYsTz11FP063dporidO3fyl7/8BaPRSEREBH/+859xcXExW62vv/56jz9zc+8PHe0H0H7fBcvuv2fOnOnwM+/pe2iuejuqNTk5mQceeKDN5e7jxo1j5cqVpKWl8fLLL6PVamltbWXatGk8/vjjpnOVH374If/85z8BmDJlCitWrDC1XY2EihBCCNVI95cQQgjVSKgIIYRQjYSKEEII1UioCCGEUI2EihBCCNVIqIgb2iuvvMITTzwBXLqxa+zYsaabKXvqzjvvJCUlRY3yVPP666/z9NNPd2vZ5cuX87e//c3MFYm+Ssb+EuI/AgMDycjIsHQZZvHggw/26HkHDhzgt7/9LXv27FG5ItFXyZGKEEII1UioCJtw66238s9//pM5c+YwZswYnnrqKS5cuMB9993H2LFjufvuu6murgYuTYC0cOFCJkyYwNy5czlw4IDpdfLz81m8eDFjx45lyZIlVFZWmtrOnz9PeHg4ra2tAFRVVfH73/+em2++mejoaB5++GHg0nAsDzzwAJMnTyY6OpoHHniA4uLia9qe3NxcFi9ezPjx45k0aRK//vWvTW3h4eGsW7eO6dOnM2nSJFavXo3RaDS1f/LJJ8yePZvo6GjuvfdeCgoKTG1nzpxhyZIlTJw4kZtuuonXX38daNvNB/Doo48yZcoUxo8fzx133MGZM2fa1VhfX8/SpUspLS1l7NixjB07lpKSEkaPHt3mfTt+/DiTJ082DfUhbmwSKsJmpKWl8c4777B9+3Z2797N0qVLeeyxx/j2228xGo38+9//pqSkhAceeICHHnqIgwcP8uSTT/Loo4+a5lh54okniIyM5MCBAzz88MNs2LChw/X97ne/o6GhgS1btvDNN99w9913A5fGUZo3bx67d+9m9+7dODo6XvOIuS+99BJTpkwhPT2dPXv2tBv1dseOHaxfv54NGzawa9cu1q9fD1wa6uWNN97g1VdfZf/+/YwfP57HH38cgNraWpYsWUJsbCx79+4lLS2NmJiYq65/6tSpbN++nf379zNixIg2gXOZi4sLb775Jn5+fmRkZJCRkYG/vz8TJ05sMxfMxo0buf3227G3t7+m90D0TRIqwmYsXryY/v374+/vz4QJE4iKimLEiBE4OjoyY8YMsrKy2LhxI1OnTuWWW25Bq9UyZcoURo4cyVdffUVhYSFHjx7lV7/6FQ4ODkRHR7eba+Ky0tJS9uzZw6pVq/Dw8MDe3p6JEycC4OXlRXx8PM7Ozri6uvLQQw+Rnp5+TdtiZ2dHYWEhpaWlbSZsumzp0qV4enoSGBjIXXfdZRo088MPP+T+++8nNDQUOzs7HnzwQU6cOEFBQQFffvkl/fv355577sHR0RFXV1dGjx591fXPnz8fV1dXHBwceOSRRzh58qRpwMau/PSnP+Xzzz8HLo18u2XLFhITE69p+0XfJSfqhc24csA9R0fHNo+dnJyor6+nsLCQ1NRUdu/ebWprbW1l0qRJlJaW4u7u3mYQx44mHSouLsbDwwMPD492bQ0NDfzpT39i7969pi63uro6DAZDpwPtXem3v/0tL730EvPnz8fDw4MlS5Ywf/58U/uVw4sHBQVRWloKXLpC7Y9//COrV682tSuKQklJCUVFRQwYMKDLdRsMBv72t7+RmppqGnYdLs1v7ubm1uXzp0+fzsqVK8nPzyc7OxtXV1eioqK6td2i75NQEX2KXq8nMTGR559/vl1bQUEBFy9epL6+3hQshYWFbYYBvywgIIDq6mrT6MdXevvtt8nOzubjjz/G19eXEydOkJSUxLWMzerr62uq8dChQyxZsoTo6GgGDhwIXJoNc9iwYaYaL4/Gq9frefDBB5k7d2671ywsLOxyqleATZs28cUXX/DOO+8QHBxMTU0N0dHRV63/au+No6Mjs2fP5vPPP+fcuXNylCLakO4v0afMnTuX3bt3s3fvXgwGA01NTRw4cIDi4mKCgoIYOXIkr7zyCs3NzRw6dKjNEc2V/Pz8mDp1KqtWraK6upqWlhZTF1ddXR2Ojo64u7tTVVXFq6++es11btu2zXRy38PDA41G02ayr7feeovq6mqKiopYt24dt912G3BpPvN//OMfphPrNTU1pvMb06ZNo6ysjHfffZfm5mZqa2vJzMxst+66ujocHBzw8vKioaGBF198scM6fXx8qKqqatc1lpiYaDrfI6EiriShIvoUvV7Pa6+9xhtvvEFMTAy33HILb731lunqqb/+9a9kZmYyadIk1q5dS1JSUoevtWbNGuzs7Jg9ezY33XQT//rXvwD45S9/SVNTE5MnT2bBggXExsZec51Hjx4lOTmZsWPH8tBDD/H0008TEhJiap8+fTrz5s0jKSmJadOmmbrGZsyYwX333cdjjz3GuHHjSEhIMN1D4urqyttvv83u3buZMmUK8fHxba58uywpKYnAwEBiY2O5/fbbGTNmTId1hoaGcvvttxMXF8eECRMoKSkBYPz48Wi1WiIjIwkKCrrm7Rd9l8ynIoSVCQ8PJy0tzdQVZq3uuusu5syZQ3JysqVLEVZEjlSEENfsyJEjZGVlmX1KZGF75ES9EGayYsUKNm3a1O73c+bMueb7WqzJk08+yc6dO3n66adxdXW1dDnCykj3lxBCCNVI95cQQgjVSKgIIYRQjYSKEEII1UioCCGEUI2EihBCCNVIqAghhFDN/w9A0pQol0EnfAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.distplot(df['medical_specialty'].value_counts());"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "positive kurtosis = 1.999\n",
      "skewness = 1.195 \n",
      "The distribution is highly skewed\n"
     ]
    }
   ],
   "source": [
    "skewness = df['medical_specialty'].value_counts().skew()\n",
    "kurtosis = df['medical_specialty'].value_counts().kurt()\n",
    "\n",
    "normality_testing(skewness,kurtosis)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We still have skewed data but the resulting distribution is way more normal now."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating the Classes file\n",
    "First, we make a dictionary with the specialties as values and the chosen encodings as keys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{3: 'Internal Medicine', 2: 'Medical Records', 4: 'Other', 1: 'Surgery'}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keys = [3,2,4,1]\n",
    "values = sorted(df['medical_specialty'].unique())\n",
    "zipObj = zip(keys,values)\n",
    "classes_dict = dict(zipObj)\n",
    "classes_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "classes_dict = sorted(classes_dict.items())\n",
    "#sort_orders = sorted(orders.items(), key=lambda x: x[1], reverse=True)\n",
    "classes_dict = dict(classes_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we order by frequency and save the classes.txt file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: 'Surgery', 2: 'Medical Records', 3: 'Internal Medicine', 4: 'Other'}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classes_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "classes_file = open(\"../data/classes.txt\",\"w\") \n",
    "for i in classes_dict:\n",
    "    classes_file.write(classes_dict[i]) \n",
    "    classes_file.write(\"\\n\")\n",
    "\n",
    "classes_file.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Consolidating standardized dataset\n",
    "First, we make a copy of the current dataframe. Then, we add the `keywords` column to the `transcription` column,  and lastly, we substitute the labels with their encodings and save the final files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>medical_specialty</th>\n",
       "      <th>sample_name</th>\n",
       "      <th>transcription</th>\n",
       "      <th>keywords</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Other</td>\n",
       "      <td>Allergic Rhinitis</td>\n",
       "      <td>SUBJECTIVE:,  This 23-year-old white female pr...</td>\n",
       "      <td>allergy / immunology, allergic rhinitis, aller...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Other</td>\n",
       "      <td>Laparoscopic Gastric Bypass Consult - 2</td>\n",
       "      <td>PAST MEDICAL HISTORY:, He has difficulty climb...</td>\n",
       "      <td>bariatrics, laparoscopic gastric bypass, weigh...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Other</td>\n",
       "      <td>Laparoscopic Gastric Bypass Consult - 1</td>\n",
       "      <td>HISTORY OF PRESENT ILLNESS: , I have seen ABC ...</td>\n",
       "      <td>bariatrics, laparoscopic gastric bypass, heart...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Internal Medicine</td>\n",
       "      <td>2-D Echocardiogram - 1</td>\n",
       "      <td>2-D M-MODE: , ,1.  Left atrial enlargement wit...</td>\n",
       "      <td>cardiovascular / pulmonary, 2-d m-mode, dopple...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Internal Medicine</td>\n",
       "      <td>2-D Echocardiogram - 2</td>\n",
       "      <td>1.  The left ventricular cavity size and wall ...</td>\n",
       "      <td>cardiovascular / pulmonary, 2-d, doppler, echo...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   medical_specialty                                sample_name  \\\n",
       "0              Other                         Allergic Rhinitis    \n",
       "1              Other   Laparoscopic Gastric Bypass Consult - 2    \n",
       "2              Other   Laparoscopic Gastric Bypass Consult - 1    \n",
       "3  Internal Medicine                    2-D Echocardiogram - 1    \n",
       "4  Internal Medicine                    2-D Echocardiogram - 2    \n",
       "\n",
       "                                       transcription  \\\n",
       "0  SUBJECTIVE:,  This 23-year-old white female pr...   \n",
       "1  PAST MEDICAL HISTORY:, He has difficulty climb...   \n",
       "2  HISTORY OF PRESENT ILLNESS: , I have seen ABC ...   \n",
       "3  2-D M-MODE: , ,1.  Left atrial enlargement wit...   \n",
       "4  1.  The left ventricular cavity size and wall ...   \n",
       "\n",
       "                                            keywords  \n",
       "0  allergy / immunology, allergic rhinitis, aller...  \n",
       "1  bariatrics, laparoscopic gastric bypass, weigh...  \n",
       "2  bariatrics, laparoscopic gastric bypass, heart...  \n",
       "3  cardiovascular / pulmonary, 2-d m-mode, dopple...  \n",
       "4  cardiovascular / pulmonary, 2-d, doppler, echo...  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Choosing features (columns)\n",
    "features = ['medical_specialty','sample_name','transcription','keywords']\n",
    "\n",
    "# X is the standard training  data variable name \n",
    "X = df[features]\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are aware there are blanks in the `keywords` column, so we will treat it as a string and merge it with the transcription column to have more keywords in our training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "medical_specialty       0\n",
       "sample_name             0\n",
       "transcription           0\n",
       "keywords             1068\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking blank values\n",
    "X.isnull().sum(axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/carlos/anaconda3/envs/syft/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>medical_specialty</th>\n",
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       "      <th>text</th>\n",
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       "      <td>bariatrics, laparoscopic gastric bypass, heart...</td>\n",
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       "    </tr>\n",
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       "      <td>Internal Medicine</td>\n",
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       "      <td>2-D M-MODE: , ,1.  Left atrial enlargement wit...</td>\n",
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       "      <td>1.  The left ventricular cavity size and wall ...</td>\n",
       "      <td>cardiovascular / pulmonary, 2-d, doppler, echo...</td>\n",
       "      <td>1.  The left ventricular cavity size and wall ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   medical_specialty                                sample_name  \\\n",
       "0              Other                         Allergic Rhinitis    \n",
       "1              Other   Laparoscopic Gastric Bypass Consult - 2    \n",
       "2              Other   Laparoscopic Gastric Bypass Consult - 1    \n",
       "3  Internal Medicine                    2-D Echocardiogram - 1    \n",
       "4  Internal Medicine                    2-D Echocardiogram - 2    \n",
       "\n",
       "                                       transcription  \\\n",
       "0  SUBJECTIVE:,  This 23-year-old white female pr...   \n",
       "1  PAST MEDICAL HISTORY:, He has difficulty climb...   \n",
       "2  HISTORY OF PRESENT ILLNESS: , I have seen ABC ...   \n",
       "3  2-D M-MODE: , ,1.  Left atrial enlargement wit...   \n",
       "4  1.  The left ventricular cavity size and wall ...   \n",
       "\n",
       "                                            keywords  \\\n",
       "0  allergy / immunology, allergic rhinitis, aller...   \n",
       "1  bariatrics, laparoscopic gastric bypass, weigh...   \n",
       "2  bariatrics, laparoscopic gastric bypass, heart...   \n",
       "3  cardiovascular / pulmonary, 2-d m-mode, dopple...   \n",
       "4  cardiovascular / pulmonary, 2-d, doppler, echo...   \n",
       "\n",
       "                                                text  \n",
       "0  SUBJECTIVE:,  This 23-year-old white female pr...  \n",
       "1  PAST MEDICAL HISTORY:, He has difficulty climb...  \n",
       "2  HISTORY OF PRESENT ILLNESS: , I have seen ABC ...  \n",
       "3  2-D M-MODE: , ,1.  Left atrial enlargement wit...  \n",
       "4  1.  The left ventricular cavity size and wall ...  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merging columns\n",
    "X['text'] = X['transcription'] + X['keywords'].astype(str)\n",
    "\n",
    "\n",
    "# Verifying our operation\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</table>\n",
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      "text/plain": [
       "   medical_specialty                                sample_name  \\\n",
       "0              Other                         Allergic Rhinitis    \n",
       "1              Other   Laparoscopic Gastric Bypass Consult - 2    \n",
       "2              Other   Laparoscopic Gastric Bypass Consult - 1    \n",
       "3  Internal Medicine                    2-D Echocardiogram - 1    \n",
       "4  Internal Medicine                    2-D Echocardiogram - 2    \n",
       "\n",
       "                                                text  \n",
       "0  SUBJECTIVE:,  This 23-year-old white female pr...  \n",
       "1  PAST MEDICAL HISTORY:, He has difficulty climb...  \n",
       "2  HISTORY OF PRESENT ILLNESS: , I have seen ABC ...  \n",
       "3  2-D M-MODE: , ,1.  Left atrial enlargement wit...  \n",
       "4  1.  The left ventricular cavity size and wall ...  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Then we will drop the original columns\n",
    "X = X.drop(['transcription', 'keywords'], axis=1)\n",
    "\n",
    "# Verifying\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "medical_specialty    0\n",
       "sample_name          0\n",
       "text                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# And confirm there are no blanks\n",
    "X.isnull().sum(axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replacing labels by their numeric encodings\n",
    "# add for loop\n",
    "for key in classes_dict.keys():\n",
    "    X.loc[(X['medical_specialty'] == classes_dict[key]),'medical_specialty']=key"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "  medical_specialty                                sample_name  \\\n",
       "0                 4                         Allergic Rhinitis    \n",
       "1                 4   Laparoscopic Gastric Bypass Consult - 2    \n",
       "2                 4   Laparoscopic Gastric Bypass Consult - 1    \n",
       "3                 3                    2-D Echocardiogram - 1    \n",
       "4                 3                    2-D Echocardiogram - 2    \n",
       "\n",
       "                                                text  \n",
       "0  SUBJECTIVE:,  This 23-year-old white female pr...  \n",
       "1  PAST MEDICAL HISTORY:, He has difficulty climb...  \n",
       "2  HISTORY OF PRESENT ILLNESS: , I have seen ABC ...  \n",
       "3  2-D M-MODE: , ,1.  Left atrial enlargement wit...  \n",
       "4  1.  The left ventricular cavity size and wall ...  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verifying it worked\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>3</th>\n",
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       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "  label                                description  \\\n",
       "0     4                         Allergic Rhinitis    \n",
       "1     4   Laparoscopic Gastric Bypass Consult - 2    \n",
       "2     4   Laparoscopic Gastric Bypass Consult - 1    \n",
       "3     3                    2-D Echocardiogram - 1    \n",
       "4     3                    2-D Echocardiogram - 2    \n",
       "\n",
       "                                                text  \n",
       "0  SUBJECTIVE:,  This 23-year-old white female pr...  \n",
       "1  PAST MEDICAL HISTORY:, He has difficulty climb...  \n",
       "2  HISTORY OF PRESENT ILLNESS: , I have seen ABC ...  \n",
       "3  2-D M-MODE: , ,1.  Left atrial enlargement wit...  \n",
       "4  1.  The left ventricular cavity size and wall ...  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Renaming columns to standard dataset names\n",
    "X = X.rename(columns={\"medical_specialty\": \"label\", \"sample_name\": \"description\"})\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Saving the full new dataframe into a Comma Separated Values (*.csv) file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "X.to_csv('../data/X.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lastly, we split the dataset into training and test datasets using sklearn's `train_test_split` at a 90/10 rate and we save the final files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing sklearn's train_test_split\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Generating the 90/10 split \n",
    "X_train, X_test = train_test_split(X, test_size=0.1)\n",
    "\n",
    "# And saving the train and test subsets into csv files\n",
    "X_train.to_csv('../data/train.csv', index = False)\n",
    "X_test.to_csv('../data/test.csv', index = False)"
   ]
  }
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