Medical_parameters_led_COVID-19_patient_to_hospital.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "btlAbfjGWw3W"
   },
   "source": [
    "## **To determine the parameters led a covid-19 patient to hospital, hence predicting the regular bed, ICU requirement for hospitals**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "H4Q3RcG1k4JA"
   },
   "source": [
    "### Importing the essential libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "tOgqievPk1oF"
   },
   "outputs": [],
   "source": [
    "#Data Analyses Libraries\n",
    "import pandas as pd                \n",
    "import numpy as np    \n",
    "from urllib.request import urlopen\n",
    "import json\n",
    "import glob\n",
    "import os\n",
    "\n",
    "#Importing Data plotting libraries\n",
    "import matplotlib.pyplot as plt     \n",
    "import plotly.express as px       \n",
    "import plotly.offline as py       \n",
    "import seaborn as sns             \n",
    "import plotly.graph_objects as go \n",
    "from plotly.subplots import make_subplots\n",
    "import matplotlib.ticker as ticker\n",
    "import matplotlib.animation as animation\n",
    "from matplotlib.ticker import MaxNLocator\n",
    "\n",
    "#Other Miscallaneous Libraries\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "from IPython.display import HTML\n",
    "import matplotlib.colors as mc\n",
    "import colorsys\n",
    "from random import randint\n",
    "import re"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IBOeI5UBn7EH"
   },
   "source": [
    "### Datasets used for analyses in this notbook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "c9CVhGRHbTXc"
   },
   "source": [
    "- [Uncover](https://www.kaggle.com/roche-data-science-coalition/uncover)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mOV0xAubo0ZX"
   },
   "source": [
    "### Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PN9FtNEnc3-7"
   },
   "source": [
    "The dataset contained data for both positive and negetive cases. But for our analysis I have to select the people those are tested positive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 273
    },
    "id": "G5B5AbvvTr9a",
    "outputId": "c46ef7ce-a109-461b-b696-47bff75fbb81"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "558\n"
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>patient_id</th>\n",
       "      <th>patient_age_quantile</th>\n",
       "      <th>sars_cov_2_exam_result</th>\n",
       "      <th>patient_addmited_to_regular_ward_1_yes_0_no</th>\n",
       "      <th>patient_addmited_to_semi_intensive_unit_1_yes_0_no</th>\n",
       "      <th>patient_addmited_to_intensive_care_unit_1_yes_0_no</th>\n",
       "      <th>hematocrit</th>\n",
       "      <th>hemoglobin</th>\n",
       "      <th>platelets</th>\n",
       "      <th>mean_platelet_volume</th>\n",
       "      <th>...</th>\n",
       "      <th>hb_saturation_arterial_blood_gases</th>\n",
       "      <th>pco2_arterial_blood_gas_analysis</th>\n",
       "      <th>base_excess_arterial_blood_gas_analysis</th>\n",
       "      <th>ph_arterial_blood_gas_analysis</th>\n",
       "      <th>total_co2_arterial_blood_gas_analysis</th>\n",
       "      <th>hco3_arterial_blood_gas_analysis</th>\n",
       "      <th>po2_arterial_blood_gas_analysis</th>\n",
       "      <th>arteiral_fio2</th>\n",
       "      <th>phosphor</th>\n",
       "      <th>cto2_arterial_blood_gas_analysis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>78511c183ae18bc</td>\n",
       "      <td>7</td>\n",
       "      <td>positive</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>d7834ed75f2da44</td>\n",
       "      <td>16</td>\n",
       "      <td>positive</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>513</th>\n",
       "      <td>b16b49f7bd3e692</td>\n",
       "      <td>10</td>\n",
       "      <td>positive</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>4382f5ea05e60c4</td>\n",
       "      <td>2</td>\n",
       "      <td>positive</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>0.991838</td>\n",
       "      <td>0.792188</td>\n",
       "      <td>-0.341548</td>\n",
       "      <td>1.469188</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>676</th>\n",
       "      <td>d3729cd2658ca64</td>\n",
       "      <td>15</td>\n",
       "      <td>positive</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 111 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          patient_id  patient_age_quantile sars_cov_2_exam_result  \\\n",
       "67   78511c183ae18bc                     7               positive   \n",
       "284  d7834ed75f2da44                    16               positive   \n",
       "513  b16b49f7bd3e692                    10               positive   \n",
       "568  4382f5ea05e60c4                     2               positive   \n",
       "676  d3729cd2658ca64                    15               positive   \n",
       "\n",
       "    patient_addmited_to_regular_ward_1_yes_0_no  \\\n",
       "67                                            f   \n",
       "284                                           t   \n",
       "513                                           f   \n",
       "568                                           f   \n",
       "676                                           f   \n",
       "\n",
       "    patient_addmited_to_semi_intensive_unit_1_yes_0_no  \\\n",
       "67                                                   f   \n",
       "284                                                  f   \n",
       "513                                                  f   \n",
       "568                                                  f   \n",
       "676                                                  f   \n",
       "\n",
       "    patient_addmited_to_intensive_care_unit_1_yes_0_no  hematocrit  \\\n",
       "67                                                   f         NaN   \n",
       "284                                                  f         NaN   \n",
       "513                                                  f         NaN   \n",
       "568                                                  f    0.991838   \n",
       "676                                                  f         NaN   \n",
       "\n",
       "     hemoglobin  platelets  mean_platelet_volume  ...  \\\n",
       "67          NaN        NaN                   NaN  ...   \n",
       "284         NaN        NaN                   NaN  ...   \n",
       "513         NaN        NaN                   NaN  ...   \n",
       "568    0.792188  -0.341548              1.469188  ...   \n",
       "676         NaN        NaN                   NaN  ...   \n",
       "\n",
       "     hb_saturation_arterial_blood_gases  pco2_arterial_blood_gas_analysis  \\\n",
       "67                                  NaN                               NaN   \n",
       "284                                 NaN                               NaN   \n",
       "513                                 NaN                               NaN   \n",
       "568                                 NaN                               NaN   \n",
       "676                                 NaN                               NaN   \n",
       "\n",
       "     base_excess_arterial_blood_gas_analysis  ph_arterial_blood_gas_analysis  \\\n",
       "67                                       NaN                             NaN   \n",
       "284                                      NaN                             NaN   \n",
       "513                                      NaN                             NaN   \n",
       "568                                      NaN                             NaN   \n",
       "676                                      NaN                             NaN   \n",
       "\n",
       "     total_co2_arterial_blood_gas_analysis  hco3_arterial_blood_gas_analysis  \\\n",
       "67                                     NaN                               NaN   \n",
       "284                                    NaN                               NaN   \n",
       "513                                    NaN                               NaN   \n",
       "568                                    NaN                               NaN   \n",
       "676                                    NaN                               NaN   \n",
       "\n",
       "     po2_arterial_blood_gas_analysis  arteiral_fio2  phosphor  \\\n",
       "67                               NaN            NaN       NaN   \n",
       "284                              NaN            NaN       NaN   \n",
       "513                              NaN            NaN       NaN   \n",
       "568                              NaN            NaN       NaN   \n",
       "676                              NaN            NaN       NaN   \n",
       "\n",
       "     cto2_arterial_blood_gas_analysis  \n",
       "67                                NaN  \n",
       "284                               NaN  \n",
       "513                               NaN  \n",
       "568                               NaN  \n",
       "676                               NaN  \n",
       "\n",
       "[5 rows x 111 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Importing the clinical spectrum data\n",
    "clinical_spectrum = pd.read_csv('dataset/diagnosis-of-covid-19-and-its-clinical-spectrum.csv')\n",
    "\n",
    "#Filtering the data to contain the values only for the confirmed COVID-19 Tests\n",
    "confirmed = clinical_spectrum['sars_cov_2_exam_result'] == 'positive'\n",
    "clinical_spectrum = clinical_spectrum[confirmed]\n",
    "\n",
    "#Printing total number of rows\n",
    "print(len(clinical_spectrum))\n",
    "\n",
    "#Viewing the dataset statisticsTo determine the parameters\n",
    "clinical_spectrum.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "172Op20wlbcN"
   },
   "source": [
    "I have tried to see how many columns does this data set has most of the  NAN values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "AJjlz5iXUBgN",
    "outputId": "d7d65db0-1e29-4a1d-ec64-29267df009d3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    }
   ],
   "source": [
    "#to check the number of nan values in the dataset for covid19 positive pateints\n",
    "for i in clinical_spectrum.columns:\n",
    "    temp = clinical_spectrum[i].isnull().sum()\n",
    "    print(temp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8--wmW9Fl_oa"
   },
   "source": [
    "From the result it seems like there are several columns that contains most of the NAN values. But I have went ahead with this dataset to see If I can find any parameter that led to covid-19 patient to hospital. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Fy_LCjl5UbcG"
   },
   "source": [
    "### Making dataframe for analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MCFc2y2avgm5"
   },
   "source": [
    "**If patient need to admit in ICU (i.e. ICU required)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "kx-JIOMOvvtB",
    "outputId": "7fd7bf68-8f2d-46c8-f90f-16433af9f344"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Parameter</th>\n",
       "      <th>a_icu_mean</th>\n",
       "      <th>a_icu_var</th>\n",
       "      <th>na_icu_mean</th>\n",
       "      <th>na_icu_var</th>\n",
       "      <th>mean_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>patient_age_quantile</td>\n",
       "      <td>14.375000</td>\n",
       "      <td>36.267857</td>\n",
       "      <td>10.576364</td>\n",
       "      <td>23.891244</td>\n",
       "      <td>3.798636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>hematocrit</td>\n",
       "      <td>0.133517</td>\n",
       "      <td>0.886866</td>\n",
       "      <td>0.260319</td>\n",
       "      <td>0.618646</td>\n",
       "      <td>-0.126803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>hemoglobin</td>\n",
       "      <td>0.345764</td>\n",
       "      <td>0.906784</td>\n",
       "      <td>0.253346</td>\n",
       "      <td>0.672792</td>\n",
       "      <td>0.092418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>platelets</td>\n",
       "      <td>-0.517413</td>\n",
       "      <td>0.526373</td>\n",
       "      <td>-0.725939</td>\n",
       "      <td>0.410866</td>\n",
       "      <td>0.208526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>mean_platelet_volume</td>\n",
       "      <td>-0.396024</td>\n",
       "      <td>0.780189</td>\n",
       "      <td>0.348793</td>\n",
       "      <td>0.763104</td>\n",
       "      <td>-0.744817</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Parameter  a_icu_mean  a_icu_var  na_icu_mean  na_icu_var  \\\n",
       "0  patient_age_quantile   14.375000  36.267857    10.576364   23.891244   \n",
       "1            hematocrit    0.133517   0.886866     0.260319    0.618646   \n",
       "2            hemoglobin    0.345764   0.906784     0.253346    0.672792   \n",
       "3             platelets   -0.517413   0.526373    -0.725939    0.410866   \n",
       "4  mean_platelet_volume   -0.396024   0.780189     0.348793    0.763104   \n",
       "\n",
       "   mean_diff  \n",
       "0   3.798636  \n",
       "1  -0.126803  \n",
       "2   0.092418  \n",
       "3   0.208526  \n",
       "4  -0.744817  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Filetering the datasets\n",
    "a_icu = clinical_spectrum['patient_addmited_to_intensive_care_unit_1_yes_0_no'] == 't'\n",
    "a_icu = clinical_spectrum[a_icu]\n",
    "\n",
    "na_icu = clinical_spectrum['patient_addmited_to_intensive_care_unit_1_yes_0_no'] == 'f'\n",
    "na_icu = clinical_spectrum[na_icu]\n",
    "\n",
    "#Taking mean and variance value of the spectra conditions\n",
    "a_icu_mean = a_icu.mean(axis = 0, skipna = True) .to_frame().reset_index()\n",
    "a_icu_var = a_icu.var(axis = 0, skipna = True) .to_frame().reset_index()\n",
    "\n",
    "na_icu_mean = na_icu.mean(axis = 0, skipna = True) .to_frame().reset_index()\n",
    "na_icu_var = na_icu.var(axis = 0, skipna = True) .to_frame().reset_index()\n",
    "\n",
    "#Make the dataframe columns\n",
    "a_icu_mean.columns = ['Parameter','a_icu_mean']\n",
    "a_icu_var.columns = ['Parameter','a_icu_var']\n",
    "\n",
    "na_icu_mean.columns = ['Parameter','na_icu_mean']\n",
    "na_icu_var.columns = ['Parameter','na_icu_var']\n",
    "\n",
    "#Merge dataframes\n",
    "a_icu_mean['a_icu_var'] = a_icu_var['a_icu_var']\n",
    "a_icu_mean['na_icu_mean'] = na_icu_mean['na_icu_mean']\n",
    "a_icu_mean['na_icu_var'] = na_icu_var['na_icu_var']\n",
    "\n",
    "#Assigning to a new name to dataframe for convenience\n",
    "result = a_icu_mean\n",
    "\n",
    "#Calculate mean difference\n",
    "result['mean_diff'] = result['a_icu_mean']-result['na_icu_mean']\n",
    "\n",
    "#View Dataframe\n",
    "result.dropna().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 363
    },
    "id": "ZSVIrwAqwPWV",
    "outputId": "f791b7c8-9f35-457f-9e77-fee1b27f4701"
   },
   "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>Parameter</th>\n",
       "      <th>a_icu_mean</th>\n",
       "      <th>a_icu_var</th>\n",
       "      <th>na_icu_mean</th>\n",
       "      <th>na_icu_var</th>\n",
       "      <th>mean_diff</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>hb_saturation_arterial_blood_gases</td>\n",
       "      <td>0.995026</td>\n",
       "      <td>0.179352</td>\n",
       "      <td>-0.363805</td>\n",
       "      <td>0.162369</td>\n",
       "      <td>1.358831</td>\n",
       "      <td>1.290487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>rods</td>\n",
       "      <td>1.911019</td>\n",
       "      <td>3.214105</td>\n",
       "      <td>-0.035800</td>\n",
       "      <td>0.717434</td>\n",
       "      <td>1.946818</td>\n",
       "      <td>1.160511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>po2_arterial_blood_gas_analysis</td>\n",
       "      <td>1.175689</td>\n",
       "      <td>1.663502</td>\n",
       "      <td>-0.229056</td>\n",
       "      <td>0.330573</td>\n",
       "      <td>1.404746</td>\n",
       "      <td>1.005931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>proteina_c_reativa_mg_dl</td>\n",
       "      <td>1.395516</td>\n",
       "      <td>2.238565</td>\n",
       "      <td>0.022732</td>\n",
       "      <td>0.527803</td>\n",
       "      <td>1.372785</td>\n",
       "      <td>0.819511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>neutrophils</td>\n",
       "      <td>0.784574</td>\n",
       "      <td>0.626487</td>\n",
       "      <td>-0.181988</td>\n",
       "      <td>0.785572</td>\n",
       "      <td>0.966561</td>\n",
       "      <td>0.684149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>ph_arterial_blood_gas_analysis</td>\n",
       "      <td>0.396815</td>\n",
       "      <td>0.005902</td>\n",
       "      <td>-0.364902</td>\n",
       "      <td>0.633572</td>\n",
       "      <td>0.761717</td>\n",
       "      <td>0.633822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>mean_corpuscular_hemoglobin_concentration_mchc</td>\n",
       "      <td>0.866512</td>\n",
       "      <td>0.381759</td>\n",
       "      <td>0.034370</td>\n",
       "      <td>0.806819</td>\n",
       "      <td>0.832142</td>\n",
       "      <td>0.594426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>total_co2_arterial_blood_gas_analysis</td>\n",
       "      <td>0.324521</td>\n",
       "      <td>0.058523</td>\n",
       "      <td>-0.332317</td>\n",
       "      <td>0.671984</td>\n",
       "      <td>0.656838</td>\n",
       "      <td>0.510736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>leukocytes</td>\n",
       "      <td>0.146101</td>\n",
       "      <td>2.188207</td>\n",
       "      <td>-0.813981</td>\n",
       "      <td>0.192053</td>\n",
       "      <td>0.960082</td>\n",
       "      <td>0.484030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>international_normalized_ratio_inr</td>\n",
       "      <td>0.720945</td>\n",
       "      <td>0.001601</td>\n",
       "      <td>0.132046</td>\n",
       "      <td>0.996035</td>\n",
       "      <td>0.588899</td>\n",
       "      <td>0.389372</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         Parameter  a_icu_mean  a_icu_var  \\\n",
       "62              hb_saturation_arterial_blood_gases    0.995026   0.179352   \n",
       "40                                            rods    1.911019   3.214105   \n",
       "68                 po2_arterial_blood_gas_analysis    1.175689   1.663502   \n",
       "19                        proteina_c_reativa_mg_dl    1.395516   2.238565   \n",
       "17                                     neutrophils    0.784574   0.626487   \n",
       "65                  ph_arterial_blood_gas_analysis    0.396815   0.005902   \n",
       "7   mean_corpuscular_hemoglobin_concentration_mchc    0.866512   0.381759   \n",
       "66           total_co2_arterial_blood_gas_analysis    0.324521   0.058523   \n",
       "8                                       leukocytes    0.146101   2.188207   \n",
       "52              international_normalized_ratio_inr    0.720945   0.001601   \n",
       "\n",
       "    na_icu_mean  na_icu_var  mean_diff     score  \n",
       "62    -0.363805    0.162369   1.358831  1.290487  \n",
       "40    -0.035800    0.717434   1.946818  1.160511  \n",
       "68    -0.229056    0.330573   1.404746  1.005931  \n",
       "19     0.022732    0.527803   1.372785  0.819511  \n",
       "17    -0.181988    0.785572   0.966561  0.684149  \n",
       "65    -0.364902    0.633572   0.761717  0.633822  \n",
       "7      0.034370    0.806819   0.832142  0.594426  \n",
       "66    -0.332317    0.671984   0.656838  0.510736  \n",
       "8     -0.813981    0.192053   0.960082  0.484030  \n",
       "52     0.132046    0.996035   0.588899  0.389372  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculating scoring functon using above formula\n",
    "c0 = 1\n",
    "c1 = -0.2\n",
    "c2 = -0.2\n",
    "result['score'] = c0*result['mean_diff'] + c1*result['a_icu_var'] + c2*result['na_icu_var']\n",
    "\n",
    "#Determining most important factor\n",
    "result.sort_values(['score'], axis=0, ascending=False, inplace=True)\n",
    "high_score = result.dropna().head(10)\n",
    "high_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "tdo_-Kc4wnHC",
    "outputId": "f6ba3a64-464f-4899-b15d-c525fcacfe4a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The difference between the hospitalised and non hospitalised patients can be\n",
      " somewhat made on the basic of the values of concentration of the following parameters:\n",
      "\n",
      "The values of these parameters vary the maximum between those who are hospitalised and who are not.\n",
      "Hence can be used to check for requirement of ICU\n",
      "\n",
      "hb_saturation_arterial_blood_gases\n",
      "rods\n",
      "po2_arterial_blood_gas_analysis\n",
      "proteina_c_reativa_mg_dl\n",
      "neutrophils\n",
      "ph_arterial_blood_gas_analysis\n",
      "mean_corpuscular_hemoglobin_concentration_mchc\n",
      "total_co2_arterial_blood_gas_analysis\n",
      "leukocytes\n",
      "international_normalized_ratio_inr\n"
     ]
    }
   ],
   "source": [
    "print('The difference between the hospitalised and non hospitalised patients can be\\n somewhat made on the basic of the values of concentration of the following parameters:\\n')\n",
    "print('The values of these parameters vary the maximum between those who are hospitalised and who are not.')\n",
    "print('Hence can be used to check for requirement of ICU\\n')\n",
    "\n",
    "for i  in list(high_score['Parameter']):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**If a patient's test result shows a_icu_mean values for the mentioned parameters with change nomore than mentioned variance, than he/she may required ICU.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 556
    },
    "id": "uP4sf7HDwvj6",
    "outputId": "071322f5-57f3-4113-8df9-45c589a55ea3"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = high_score.plot.bar(x='Parameter', y=['a_icu_mean','na_icu_mean'], rot=60,figsize=(10,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "a_uCz-rWvUoO"
   },
   "source": [
    "**If patient need to admit in hospital without ICU(i.e. normal beds requirement)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "bu3FYYbm64su",
    "outputId": "5ae1a4d6-3f65-4988-9fbb-e541406d3d07",
    "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",
       "    .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>Parameter</th>\n",
       "      <th>a_rward_mean</th>\n",
       "      <th>a_rward_var</th>\n",
       "      <th>na_rward_mean</th>\n",
       "      <th>na_rward_var</th>\n",
       "      <th>mean_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>patient_age_quantile</td>\n",
       "      <td>14.333333</td>\n",
       "      <td>13.885714</td>\n",
       "      <td>10.375479</td>\n",
       "      <td>23.935520</td>\n",
       "      <td>3.957854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>hematocrit</td>\n",
       "      <td>0.272967</td>\n",
       "      <td>0.514790</td>\n",
       "      <td>0.236107</td>\n",
       "      <td>0.703542</td>\n",
       "      <td>0.036861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>hemoglobin</td>\n",
       "      <td>0.225963</td>\n",
       "      <td>0.548848</td>\n",
       "      <td>0.279751</td>\n",
       "      <td>0.761330</td>\n",
       "      <td>-0.053788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>platelets</td>\n",
       "      <td>-0.864491</td>\n",
       "      <td>0.217085</td>\n",
       "      <td>-0.629348</td>\n",
       "      <td>0.504574</td>\n",
       "      <td>-0.235143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>mean_platelet_volume</td>\n",
       "      <td>0.256638</td>\n",
       "      <td>0.522645</td>\n",
       "      <td>0.284020</td>\n",
       "      <td>0.950465</td>\n",
       "      <td>-0.027381</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Parameter  a_rward_mean  a_rward_var  na_rward_mean  \\\n",
       "0  patient_age_quantile     14.333333    13.885714      10.375479   \n",
       "1            hematocrit      0.272967     0.514790       0.236107   \n",
       "2            hemoglobin      0.225963     0.548848       0.279751   \n",
       "3             platelets     -0.864491     0.217085      -0.629348   \n",
       "4  mean_platelet_volume      0.256638     0.522645       0.284020   \n",
       "\n",
       "   na_rward_var  mean_diff  \n",
       "0     23.935520   3.957854  \n",
       "1      0.703542   0.036861  \n",
       "2      0.761330  -0.053788  \n",
       "3      0.504574  -0.235143  \n",
       "4      0.950465  -0.027381  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Filetering the datasets\n",
    "a_rward = clinical_spectrum['patient_addmited_to_regular_ward_1_yes_0_no'] == 't'\n",
    "a_rward = clinical_spectrum[a_rward]\n",
    "\n",
    "na_rward = clinical_spectrum['patient_addmited_to_regular_ward_1_yes_0_no'] == 'f'\n",
    "na_rward = clinical_spectrum[na_rward]\n",
    "\n",
    "#Taking mean and variance value of the spectra conditions\n",
    "a_rward_mean = a_rward.mean(axis = 0, skipna = True) .to_frame().reset_index()\n",
    "a_rward_var = a_rward.var(axis = 0, skipna = True) .to_frame().reset_index()\n",
    "\n",
    "na_rward_mean = na_rward.mean(axis = 0, skipna = True) .to_frame().reset_index()\n",
    "na_rward_var = na_rward.var(axis = 0, skipna = True) .to_frame().reset_index()\n",
    "\n",
    "#Make the dataframe columns\n",
    "a_rward_mean.columns = ['Parameter','a_rward_mean']\n",
    "a_rward_var.columns = ['Parameter','a_rward_var']\n",
    "\n",
    "na_rward_mean.columns = ['Parameter','na_rward_mean']\n",
    "na_rward_var.columns = ['Parameter','na_rward_var']\n",
    "\n",
    "#Merge dataframes\n",
    "a_rward_mean['a_rward_var'] = a_rward_var['a_rward_var']\n",
    "a_rward_mean['na_rward_mean'] = na_rward_mean['na_rward_mean']\n",
    "a_rward_mean['na_rward_var'] = na_rward_var['na_rward_var']\n",
    "\n",
    "#Assigning to a new name to dataframe for convenience\n",
    "result = a_rward_mean\n",
    "\n",
    "#Calculate mean difference\n",
    "result['mean_diff'] = result['a_rward_mean']-result['na_rward_mean']\n",
    "\n",
    "#View Dataframe\n",
    "result.dropna().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3opGuz2gQ5mh"
   },
   "source": [
    "I tried  a scoring function that is as follows: \n",
    "**score = c0\\*mean_diff + c1\\*a_rward_var + c2\\*na_rward_var** \\\n",
    "I have tried different scoring coefficient. But the below mentioned one gives a decent result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 363
    },
    "id": "mcLWCOZmSZNn",
    "outputId": "a29aa929-2d6e-4a6f-925c-da4a474e6735"
   },
   "outputs": [
    {
     "data": {
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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>Parameter</th>\n",
       "      <th>a_rward_mean</th>\n",
       "      <th>a_rward_var</th>\n",
       "      <th>na_rward_mean</th>\n",
       "      <th>na_rward_var</th>\n",
       "      <th>mean_diff</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>cto2_arterial_blood_gas_analysis</td>\n",
       "      <td>0.902048</td>\n",
       "      <td>0.939896</td>\n",
       "      <td>-0.112756</td>\n",
       "      <td>0.517282</td>\n",
       "      <td>1.014804</td>\n",
       "      <td>0.723368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>hb_saturation_arterial_blood_gases</td>\n",
       "      <td>0.687012</td>\n",
       "      <td>0.418547</td>\n",
       "      <td>-0.208156</td>\n",
       "      <td>1.426671</td>\n",
       "      <td>0.895168</td>\n",
       "      <td>0.526124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>neutrophils</td>\n",
       "      <td>0.272185</td>\n",
       "      <td>0.498211</td>\n",
       "      <td>-0.273755</td>\n",
       "      <td>0.926469</td>\n",
       "      <td>0.545941</td>\n",
       "      <td>0.261005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>base_excess_venous_blood_gas_analysis</td>\n",
       "      <td>0.415236</td>\n",
       "      <td>0.146613</td>\n",
       "      <td>0.080306</td>\n",
       "      <td>0.489202</td>\n",
       "      <td>0.334930</td>\n",
       "      <td>0.207766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>ph_venous_blood_gas_analysis</td>\n",
       "      <td>0.426555</td>\n",
       "      <td>0.552741</td>\n",
       "      <td>-0.000801</td>\n",
       "      <td>0.651337</td>\n",
       "      <td>0.427356</td>\n",
       "      <td>0.186541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>creatinine</td>\n",
       "      <td>0.398583</td>\n",
       "      <td>0.463932</td>\n",
       "      <td>0.061387</td>\n",
       "      <td>0.595703</td>\n",
       "      <td>0.337196</td>\n",
       "      <td>0.125269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>urea</td>\n",
       "      <td>-0.001807</td>\n",
       "      <td>0.468767</td>\n",
       "      <td>-0.286231</td>\n",
       "      <td>0.425411</td>\n",
       "      <td>0.284423</td>\n",
       "      <td>0.105588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>urine_red_blood_cells</td>\n",
       "      <td>-0.071212</td>\n",
       "      <td>0.079228</td>\n",
       "      <td>-0.192306</td>\n",
       "      <td>0.000171</td>\n",
       "      <td>0.121094</td>\n",
       "      <td>0.105214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>relationship_patient_normal</td>\n",
       "      <td>0.194869</td>\n",
       "      <td>0.104855</td>\n",
       "      <td>0.023613</td>\n",
       "      <td>0.317487</td>\n",
       "      <td>0.171256</td>\n",
       "      <td>0.086787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>hco3_venous_blood_gas_analysis</td>\n",
       "      <td>0.262490</td>\n",
       "      <td>0.180445</td>\n",
       "      <td>0.033114</td>\n",
       "      <td>0.592234</td>\n",
       "      <td>0.229376</td>\n",
       "      <td>0.074840</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Parameter  a_rward_mean  a_rward_var  \\\n",
       "70       cto2_arterial_blood_gas_analysis      0.902048     0.939896   \n",
       "61     hb_saturation_arterial_blood_gases      0.687012     0.418547   \n",
       "17                            neutrophils      0.272185     0.498211   \n",
       "34  base_excess_venous_blood_gas_analysis      0.415236     0.146613   \n",
       "38           ph_venous_blood_gas_analysis      0.426555     0.552741   \n",
       "20                             creatinine      0.398583     0.463932   \n",
       "18                                   urea     -0.001807     0.468767   \n",
       "48                  urine_red_blood_cells     -0.071212     0.079228   \n",
       "50            relationship_patient_normal      0.194869     0.104855   \n",
       "39         hco3_venous_blood_gas_analysis      0.262490     0.180445   \n",
       "\n",
       "    na_rward_mean  na_rward_var  mean_diff     score  \n",
       "70      -0.112756      0.517282   1.014804  0.723368  \n",
       "61      -0.208156      1.426671   0.895168  0.526124  \n",
       "17      -0.273755      0.926469   0.545941  0.261005  \n",
       "34       0.080306      0.489202   0.334930  0.207766  \n",
       "38      -0.000801      0.651337   0.427356  0.186541  \n",
       "20       0.061387      0.595703   0.337196  0.125269  \n",
       "18      -0.286231      0.425411   0.284423  0.105588  \n",
       "48      -0.192306      0.000171   0.121094  0.105214  \n",
       "50       0.023613      0.317487   0.171256  0.086787  \n",
       "39       0.033114      0.592234   0.229376  0.074840  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculating scoring functon using above formula\n",
    "c0 = 1\n",
    "c1 = -0.2\n",
    "c2 = -0.2\n",
    "result['score'] = c0*result['mean_diff'] + c1*result['a_rward_var'] + c2*result['na_rward_var']\n",
    "\n",
    "#Determining most important factor\n",
    "result.sort_values(['score'], axis=0, ascending=False, inplace=True)\n",
    "high_score = result.dropna().head(10)\n",
    "high_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Jm4e6FvY1YAS",
    "outputId": "aeefa66f-10d0-4dcd-aa16-0ed55c83818f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The difference between the hospitalised and non hospitalised patients can be\n",
      " somewhat made on the basic of the values of concentration of the following parameters:\n",
      "\n",
      "The values of these parameters vary the maximum between those who are hospitalised and who are not.\n",
      "hence can be used to check for requirement of beds\n",
      "\n",
      "cto2_arterial_blood_gas_analysis\n",
      "hb_saturation_arterial_blood_gases\n",
      "neutrophils\n",
      "base_excess_venous_blood_gas_analysis\n",
      "ph_venous_blood_gas_analysis\n",
      "creatinine\n",
      "urea\n",
      "urine_red_blood_cells\n",
      "relationship_patient_normal\n",
      "hco3_venous_blood_gas_analysis\n"
     ]
    }
   ],
   "source": [
    "print('The difference between the hospitalised and non hospitalised patients can be\\n somewhat made on the basic of the values of concentration of the following parameters:\\n')\n",
    "print('The values of these parameters vary the maximum between those who are hospitalised and who are not.')\n",
    "print('hence can be used to check for requirement of beds\\n')\n",
    "\n",
    "for i  in list(high_score['Parameter']):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mQ0tXUYpn3WB"
   },
   "source": [
    "I have selected top scorer Parameters (i.e. rows) and plot them in following graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 506
    },
    "id": "qfOvFoOR1X9o",
    "outputId": "a2f4a2c9-65be-4b7c-e9a3-3e21d4c494ac",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = high_score.plot.bar(x='Parameter', y=['a_rward_mean','na_rward_mean'], rot=60,figsize=(10,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**If a patient's test result shows a_icu_mean values for the mentioned parameters with change nomore than mentioned variance, than he/she may required normal hospital bed.**"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "himanshu.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}