NLP_Project_Code.ipynb 16 KB
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{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
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    "For this project, our goal is create an NLP model to automatically assign ICD-9 encodings, given the clinical notes for each encounter)."
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   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 10,
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   "metadata": {},
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All modules imported successfully\n"
     ]
    }
   ],
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   "source": [
    "#imports\n",
    "import pandas as pd\n",
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    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix\n",
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    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
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    "\n",
    "\n",
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    "print(\"All modules imported successfully\")"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 2,
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   "metadata": {},
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fetching data\n",
      "Done fetching all the data\n"
     ]
    },
    {
     "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>TEXT</th>\n",
       "      <th>ICD9_CODE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Admission Date:  [**2141-9-18**]              ...</td>\n",
       "      <td>40301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PATIENT/TEST INFORMATION:\\nIndication: Pericar...</td>\n",
       "      <td>40301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sinus rhythm\\nRightward axis\\nSince previous t...</td>\n",
       "      <td>40301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Chief Complaint:  hypotension, altered mental ...</td>\n",
       "      <td>40301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chief Complaint:  hypotension, altered mental ...</td>\n",
       "      <td>40301</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                TEXT ICD9_CODE\n",
       "0  Admission Date:  [**2141-9-18**]              ...     40301\n",
       "1  PATIENT/TEST INFORMATION:\\nIndication: Pericar...     40301\n",
       "2  Sinus rhythm\\nRightward axis\\nSince previous t...     40301\n",
       "3  Chief Complaint:  hypotension, altered mental ...     40301\n",
       "4  Chief Complaint:  hypotension, altered mental ...     40301"
      ]
     },
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     "execution_count": 2,
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
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   "source": [
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    "print(\"Fetching data\")\n",
    "\"\"\"\n",
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    "#This code is to import data from the MIMIC-III files.\n",
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    "\n",
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    "#For performance reasons, this code has been commented out\n",
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    "\n",
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    "diagnoses = pd.read_csv(\"DIAGNOSES_ICD.csv\")\n",
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    "note_events = pd.read_csv(\"NOTEEVENTS.csv\", engine=\"python\", on_bad_lines='skip')\n",
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    "full_dataset = pd.merge(diagnoses, note_events, on =[\"HADM_ID\", \"SUBJECT_ID\"])\n",
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    "full_dataset = full_dataset[:40000]\n",
    "\n",
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    "print(full_dataset.head())\n",
    "full_dataset.to_csv(\"ICDdata40k.csv\")\n",
    "\n",
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    "\"\"\"\n",
    "\n",
    "full_dataset = pd.read_csv(\"ICDdata40k.csv\")[[ \"TEXT\", \"ICD9_CODE\"]]\n",
    "full_dataset= full_dataset[:1000]\n",
    "print(\"Done fetching all the data\")\n",
    "full_dataset.head()"
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   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "metadata": {},
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   "outputs": [],
   "source": [
    "import nltk"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 3,
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   "metadata": {},
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Transforming descriptions into TF-IDF vectors\n",
      "(1000, 9878)\n",
      "Done transforming data\n"
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     ]
    }
   ],
   "source": [
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    "#tf-idf vectorization\n",
    "print(\"Transforming descriptions into TF-IDF vectors\")\n",
    "texts = full_dataset.TEXT\n",
    "tfidf = TfidfVectorizer(sublinear_tf=True, min_df=5, norm='l2', encoding='latin-1', ngram_range=(1, 2), stop_words='english')\n",
    "feature_vectors = tfidf.fit_transform(texts)\n",
    "feature_vectors = feature_vectors.toarray()\n",
    "icd_codes = full_dataset.ICD9_CODE\n",
    "print(feature_vectors.shape)\n",
    "print(\"Done transforming data\")\n"
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   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 4,
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   "metadata": {},
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   "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>Model</th>\n",
       "      <th>Average_Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LinearSVC</td>\n",
       "      <td>0.038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MultinomialNB</td>\n",
       "      <td>0.041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.041</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Model  Average_Accuracy\n",
       "0               LinearSVC             0.038\n",
       "1           MultinomialNB             0.041\n",
       "2      LogisticRegression             0.038\n",
       "3  RandomForestClassifier             0.041"
      ]
     },
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     "execution_count": 4,
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
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   "source": [
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    "#Evaluating different models\n",
    "models = [\n",
    "    \n",
    "    LinearSVC(),\n",
    "    MultinomialNB(),\n",
    "    LogisticRegression(random_state=0),\n",
    "    RandomForestClassifier(n_estimators=200, max_depth=3, random_state=0)\n",
    "]\n",
    "\n",
    "CV = 2\n",
    "\n",
    "cross_val_results = pd.DataFrame(columns=[\"Model\", \"Average_Accuracy\"])\n",
    "\n",
    "for model in models:\n",
    "    \n",
    "    model_name = model.__class__.__name__\n",
    "    accuracies = cross_val_score(model, feature_vectors, icd_codes, scoring='accuracy', cv=CV)\n",
    "    avg_accuracy = sum(accuracies)/len(accuracies)\n",
    "    cross_val_results = cross_val_results.append({\"Model\": model_name, \"Average_Accuracy\": avg_accuracy }, ignore_index=True)\n",
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    "\n",
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    "\n",
    "cross_val_results"
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   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 8,
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   "metadata": {},
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done exploring Random Forest Classifier\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
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   "source": [
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    "#Random Forest Classifier appears to have a slight edge over the other models, so it is time to explore it in more depth\n",
    "from matplotlib.pyplot import matshow\n",
    "model = RandomForestClassifier(n_estimators=200, max_depth=3, random_state=0)\n",
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    "X_train, X_test, y_train, y_test = train_test_split(feature_vectors, icd_codes, test_size=0.10, random_state=0)\n",
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    "\n",
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    "model.fit(X_train, y_train)\n",
    "y_pred = model.predict(X_test)\n",
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    "\n",
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    "conf_mat = confusion_matrix(y_test, y_pred)\n",
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    "\n",
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    "matshow(conf_mat)\n",
    "print(\"Done exploring Random Forest Classifier\")\n"
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   ]
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  },
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  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "       40301       0.00      0.00      0.00       2.0\n",
      "         486       0.00      0.00      0.00       2.0\n",
      "       58281       0.00      0.00      0.00       3.0\n",
      "        5855       0.00      0.00      0.00       9.0\n",
      "        4254       0.00      0.00      0.00       7.0\n",
      "        2762       0.00      0.00      0.00       4.0\n",
      "        7100       0.00      0.00      0.00       5.0\n",
      "        2767       0.00      0.00      0.00       5.0\n",
      "        7243       0.00      0.00      0.00       4.0\n",
      "       45829       0.00      0.00      0.00      10.0\n",
      "        2875       0.00      0.00      0.00       2.0\n",
      "       28521       0.00      0.00      0.00       7.0\n",
      "       28529       0.00      0.00      0.00       2.0\n",
      "       27541       0.00      0.00      0.00       5.0\n",
      "        5856       0.00      0.00      0.00       1.0\n",
      "       58381       0.00      0.00      0.00       8.0\n",
      "        5589       0.00      0.00      0.00       2.0\n",
      "       32723       0.00      0.00      0.00       1.0\n",
      "       22804       0.00      0.00      0.00       5.0\n",
      "       33829       0.00      0.00      0.00       5.0\n",
      "       78900       0.00      0.00      0.00       5.0\n",
      "       79092       0.00      0.00      0.00       1.0\n",
      "       V4511       0.00      0.00      0.00       5.0\n",
      "\n",
      "    accuracy                           0.00     100.0\n",
      "   macro avg       0.00      0.00      0.00     100.0\n",
      "weighted avg       0.00      0.00      0.00     100.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "#Display metrics on Random Forest Classifier\n",
    "print(metrics.classification_report(y_test, y_pred, target_names=full_dataset['ICD9_CODE'].unique()))"
   ]
  },
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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  }
 ],
 "metadata": {
  "kernelspec": {
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   "display_name": "Python 3",
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   "language": "python",
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   "name": "python3"
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  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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   "version": "3.8.8"
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  }
 },
 "nbformat": 4,
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 "nbformat_minor": 4
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}