3_LTC_Odds_ratios_CI.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "aa76ed93",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import statsmodels.api as sm \n",
    "from sklearn.preprocessing import PowerTransformer\n",
    "from imblearn.over_sampling import RandomOverSampler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0f7e71b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# read the database \n",
    "ltc_df=pd.read_hdf(\"ltc_df.h5\",key='ltc_df')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7b8a8723",
   "metadata": {},
   "outputs": [],
   "source": [
    "# to expand the number of columns and rows that we can see while dispalying a datafram\n",
    "pd.set_option('display.max_columns', ltc_df.columns.shape[0]+1)\n",
    "pd.set_option('display.max_rows', 200)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a4366c5",
   "metadata": {},
   "source": [
    "# Pre-processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e7594d3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "ltc_df_desc_t2_=ltc_df.copy()\n",
    "# adding year_mont togerher\n",
    "ltc_df_desc_t2_['y_m_']=ltc_df_desc_t2_['specimen_year_collected'].astype(str)+'-'+ltc_df_desc_t2_['specimen_month_collected'].astype(str)\n",
    "ltc_df_desc_t2_['specimen_year_month_collected_con']=ltc_df_desc_t2_[['specimen_year_collected','specimen_month_collected']].apply(lambda x: x[1]-2 if x[0]==2020 else x[1]+10, axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "4d60f298",
   "metadata": {},
   "outputs": [],
   "source": [
    "# list of comorbidities\n",
    "list_of_cmbdt=[\n",
    "\n",
    "'chf_2_years_full',\n",
    "'htn_unc_2_years_full', \n",
    "'cancer_mets_2_years_full',\n",
    "'fluid_elec_dis_2_years_full', \n",
    "'card_arrh_2_years_full', \n",
    "'valv_dis_2_years_full', \n",
    "'pcd_2_years_full',\n",
    "'pvd_2_years_full', \n",
    "'htn_c_2_years_full',\n",
    "'paral_2_years_full', 'oth_neur_dis_2_years_full', 'cpd_2_years_full',\n",
    "'diab_2_years_full', 'hypothyroidism_2_years_full',\n",
    "'ren_fail_2_years_full', 'liver_dis_2_years_full',\n",
    "'pep_ulc_exc_bld_2_years_full', 'aids_hiv_2_years_full',\n",
    "'lymph_2_years_full', \n",
    "'tumour_no_mets_2_years_full', 'rheum_col_vasc_dis_2_years_full',\n",
    "'coag_2_years_full', 'obes_2_years_full', 'wt_loss_2_years_full',\n",
    "'anemia_2_years_full',\n",
    "'alc_abuse_2_years_full', 'drug_abuse_2_years_full',\n",
    "'psych_2_years_full', 'depress_2_years_full']\n",
    "\n",
    "# list of continious features\n",
    "list_of_continious_features=[\n",
    "\n",
    "'num_elixhauser_2_years_full',\n",
    "'elix_index',\n",
    "'num_admits_1_year', \n",
    "'num_scu_admits_1_year',\n",
    "'num_procs_dad_1_year', \n",
    "'num_procs_nacrs_1_year',\n",
    "'num_procs_tot_1_year',\n",
    "'spec_pat_num_age' ,\n",
    "'specimen_year_month_collected_con',\n",
    "]\n",
    "\n",
    "# list of categorical features\n",
    "list_of_categorical_features=[\n",
    "\n",
    "\n",
    "# collection \n",
    "'collected_in_hospital', \n",
    "'collected_in_ed',\n",
    "'collected_in_ltc', \n",
    "'collected_in_dsl',\n",
    "# resident                           \n",
    "'ltc_resident_during_collection', \n",
    "'dsl_resident_during_collection',\n",
    "                      \n",
    "]\n",
    "\n",
    "cat_vars=[\n",
    "    \n",
    "'spec_pat_agecat',\n",
    "'patient_gender',  \n",
    "'num_elixhauser_2_years_full_cat',\n",
    "'symptomatic_during_collection', \n",
    "'interp_result',\n",
    "'y_m_'\n",
    "]\n",
    "\n",
    "\n",
    "ltc_df_desc_t2=ltc_df_desc_t2_[list_of_continious_features+list_of_categorical_features+cat_vars+['died_within_60_days']]\n",
    "ltc_df_desc_t2_cmd=ltc_df_desc_t2_[list_of_cmbdt+['died_within_60_days']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b4b8640",
   "metadata": {},
   "source": [
    "### Create dummy variables\n",
    "That is variables with only two values, zero and one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d95e005f",
   "metadata": {},
   "outputs": [],
   "source": [
    "ltc_df_desc_t2[cat_vars]\n",
    "\n",
    "for var in cat_vars:\n",
    "    cat_list='var'+'_'+var\n",
    "    cat_list = pd.get_dummies(ltc_df_desc_t2[var], prefix=var)\n",
    "    data1=ltc_df_desc_t2.join(cat_list)\n",
    "    ltc_df_desc_t2=data1\n",
    "    \n",
    "data_vars=ltc_df_desc_t2.columns.values.tolist()\n",
    "data_final=ltc_df_desc_t2\n",
    "data_final.columns.values\n",
    "\n",
    "\n",
    "ltc_df_LR_t2_1=data_final[list_of_continious_features+['dsl_resident_during_collection','patient_gender_M','interp_result_Positive','num_elixhauser_2_years_full_cat_0','num_elixhauser_2_years_full_cat_1+']] # base is dsl\n",
    "ltc_df_LR_t2_2=data_final[['collected_in_ed', 'collected_in_hospital', 'collected_in_dsl']] # Base is LTC\n",
    "ltc_df_LR_t2_3=data_final[['spec_pat_agecat_30-39', 'spec_pat_agecat_40-49','spec_pat_agecat_50-59', 'spec_pat_agecat_60-69','spec_pat_agecat_70-79', 'spec_pat_agecat_18-29']] # Base is 80+\n",
    "ltc_df_LR_t2_4=data_final[['symptomatic_during_collection_U','symptomatic_during_collection_Y',]] # base is N\n",
    "ltc_df_LR_t2_5=data_final[['y_m__2020-10','y_m__2020-11', 'y_m__2020-12', 'y_m__2020-3', 'y_m__2020-4','y_m__2020-5', 'y_m__2021-3', 'y_m__2020-7', 'y_m__2020-8','y_m__2020-9', 'y_m__2021-1', 'y_m__2021-2']]\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "ltc_df_desc_t2_cmd_1=ltc_df_desc_t2_cmd.copy()\n",
    "\n",
    "for var in ltc_df_desc_t2_cmd.columns[:-1]:\n",
    "    cat_list='var'+'_'+var\n",
    "    cat_list = pd.get_dummies(ltc_df_desc_t2_cmd_1[var], prefix=var)\n",
    "    data1=ltc_df_desc_t2_cmd_1.join(cat_list.iloc[:,1])\n",
    "    ltc_df_desc_t2_cmd_1=data1\n",
    "    \n",
    "\n",
    "ltc_df_desc_t2_cmd_1=ltc_df_desc_t2_cmd_1.iloc[:,29:]\n",
    "\n",
    "data_final=data_final.join(ltc_df_desc_t2_cmd_1.iloc[:,1:])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fcf9925",
   "metadata": {},
   "source": [
    "# Odds ratio with Imbalanced classes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "34216da1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Odds ratio for comorbidities\n",
    "y=ltc_df_desc_t2_cmd_1['died_within_60_days']\n",
    "cmd=ltc_df_desc_t2_cmd_1.iloc[:,1:]\n",
    "model_odds = pd.DataFrame()#columns= [['Feature','OR','p-value','2.5%', '97.5%', 'LLR p-value','Pseudo R-squared']])\n",
    "\n",
    "i=0\n",
    "for feature in cmd.columns.values:\n",
    "    \n",
    "    X=cmd[feature]\n",
    "    X=sm.add_constant(X)\n",
    "    logit_model=sm.Logit(y,X)\n",
    "    result=logit_model.fit()\n",
    "\n",
    "    dict = {'Feature': feature , \n",
    "            'OR': np.round( np.exp(result.params),2)[1], \n",
    "            'Coef':np.round( result.params.values,3)[1], \n",
    "            'p-value': np.round( result.pvalues.values,2)[1]  ,\n",
    "             '2.5%': np.round( np.exp(result.conf_int())[0].values,3)[1] , \n",
    "            '97.5%': np.round( np.exp(result.conf_int())[1].values,3)[1],\n",
    "             'LLR p-value' :result.llr_pvalue, \n",
    "            'Pseudo R-squared':result.prsquared\n",
    "           \n",
    "           }\n",
    "\n",
    "    model_odds = model_odds.append(dict, ignore_index = True)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "be3824af",
   "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>Feature</th>\n",
       "      <th>OR</th>\n",
       "      <th>p-value</th>\n",
       "      <th>2.5%</th>\n",
       "      <th>97.5%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>chf_2_years_full_1</td>\n",
       "      <td>1.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.457</td>\n",
       "      <td>1.750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>htn_unc_2_years_full_1</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.898</td>\n",
       "      <td>1.057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>cancer_mets_2_years_full_1</td>\n",
       "      <td>1.92</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.476</td>\n",
       "      <td>2.499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>fluid_elec_dis_2_years_full_1</td>\n",
       "      <td>1.47</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.350</td>\n",
       "      <td>1.611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>card_arrh_2_years_full_1</td>\n",
       "      <td>1.36</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.246</td>\n",
       "      <td>1.493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>valv_dis_2_years_full_1</td>\n",
       "      <td>1.48</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.196</td>\n",
       "      <td>1.830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>pcd_2_years_full_1</td>\n",
       "      <td>1.59</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.327</td>\n",
       "      <td>1.902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>pvd_2_years_full_1</td>\n",
       "      <td>1.35</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.188</td>\n",
       "      <td>1.535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>htn_c_2_years_full_1</td>\n",
       "      <td>1.22</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.824</td>\n",
       "      <td>1.797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>paral_2_years_full_1</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.507</td>\n",
       "      <td>0.788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>oth_neur_dis_2_years_full_1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.920</td>\n",
       "      <td>1.085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>cpd_2_years_full_1</td>\n",
       "      <td>1.32</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.206</td>\n",
       "      <td>1.455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>diab_2_years_full_1</td>\n",
       "      <td>1.12</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.029</td>\n",
       "      <td>1.228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>hypothyroidism_2_years_full_1</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.875</td>\n",
       "      <td>1.118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>ren_fail_2_years_full_1</td>\n",
       "      <td>1.33</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.193</td>\n",
       "      <td>1.484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>liver_dis_2_years_full_1</td>\n",
       "      <td>1.24</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.980</td>\n",
       "      <td>1.559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>pep_ulc_exc_bld_2_years_full_1</td>\n",
       "      <td>1.57</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.175</td>\n",
       "      <td>2.086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>aids_hiv_2_years_full_1</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.039</td>\n",
       "      <td>2.096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>lymph_2_years_full_1</td>\n",
       "      <td>1.42</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.995</td>\n",
       "      <td>2.040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>tumour_no_mets_2_years_full_1</td>\n",
       "      <td>1.41</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.244</td>\n",
       "      <td>1.599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>rheum_col_vasc_dis_2_years_full_1</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.864</td>\n",
       "      <td>1.037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>coag_2_years_full_1</td>\n",
       "      <td>1.36</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.113</td>\n",
       "      <td>1.657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>obes_2_years_full_1</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.549</td>\n",
       "      <td>0.884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>wt_loss_2_years_full_1</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.065</td>\n",
       "      <td>1.478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>anemia_2_years_full_1</td>\n",
       "      <td>1.38</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.222</td>\n",
       "      <td>1.554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>alc_abuse_2_years_full_1</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.588</td>\n",
       "      <td>0.894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>drug_abuse_2_years_full_1</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.530</td>\n",
       "      <td>0.833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>psych_2_years_full_1</td>\n",
       "      <td>1.10</td>\n",
       "      <td>0.05</td>\n",
       "      <td>1.001</td>\n",
       "      <td>1.202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>depress_2_years_full_1</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.777</td>\n",
       "      <td>0.915</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Feature    OR  p-value   2.5%  97.5%\n",
       "0                  chf_2_years_full_1  1.60     0.00  1.457  1.750\n",
       "1              htn_unc_2_years_full_1  0.97     0.53  0.898  1.057\n",
       "2          cancer_mets_2_years_full_1  1.92     0.00  1.476  2.499\n",
       "3       fluid_elec_dis_2_years_full_1  1.47     0.00  1.350  1.611\n",
       "4            card_arrh_2_years_full_1  1.36     0.00  1.246  1.493\n",
       "5             valv_dis_2_years_full_1  1.48     0.00  1.196  1.830\n",
       "6                  pcd_2_years_full_1  1.59     0.00  1.327  1.902\n",
       "7                  pvd_2_years_full_1  1.35     0.00  1.188  1.535\n",
       "8                htn_c_2_years_full_1  1.22     0.32  0.824  1.797\n",
       "9                paral_2_years_full_1  0.63     0.00  0.507  0.788\n",
       "10        oth_neur_dis_2_years_full_1  1.00     0.99  0.920  1.085\n",
       "11                 cpd_2_years_full_1  1.32     0.00  1.206  1.455\n",
       "12                diab_2_years_full_1  1.12     0.01  1.029  1.228\n",
       "13      hypothyroidism_2_years_full_1  0.99     0.86  0.875  1.118\n",
       "14            ren_fail_2_years_full_1  1.33     0.00  1.193  1.484\n",
       "15           liver_dis_2_years_full_1  1.24     0.07  0.980  1.559\n",
       "16     pep_ulc_exc_bld_2_years_full_1  1.57     0.00  1.175  2.086\n",
       "17            aids_hiv_2_years_full_1  0.29     0.22  0.039  2.096\n",
       "18               lymph_2_years_full_1  1.42     0.05  0.995  2.040\n",
       "19      tumour_no_mets_2_years_full_1  1.41     0.00  1.244  1.599\n",
       "20  rheum_col_vasc_dis_2_years_full_1  0.95     0.24  0.864  1.037\n",
       "21                coag_2_years_full_1  1.36     0.00  1.113  1.657\n",
       "22                obes_2_years_full_1  0.70     0.00  0.549  0.884\n",
       "23             wt_loss_2_years_full_1  1.25     0.01  1.065  1.478\n",
       "24              anemia_2_years_full_1  1.38     0.00  1.222  1.554\n",
       "25           alc_abuse_2_years_full_1  0.72     0.00  0.588  0.894\n",
       "26          drug_abuse_2_years_full_1  0.66     0.00  0.530  0.833\n",
       "27               psych_2_years_full_1  1.10     0.05  1.001  1.202\n",
       "28             depress_2_years_full_1  0.84     0.00  0.777  0.915"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_odds[['Feature','OR','p-value','2.5%', '97.5%']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b699a61b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculation odds ratio for other categorical variables\n",
    "y=data_final['died_within_60_days']\n",
    "model_odds = pd.DataFrame()#columns= [['Feature','OR','p-value','2.5%', '97.5%', 'LLR p-value','Pseudo R-squared']])\n",
    "\n",
    "i=0\n",
    "for feature in ltc_df_LR_t2_1.columns.values:\n",
    "    X=ltc_df_LR_t2_1[feature]\n",
    "    X=sm.add_constant(X)\n",
    "    logit_model=sm.Logit(y,X)\n",
    "    result=logit_model.fit()\n",
    "    dict = {'Feature': feature , \n",
    "            'OR': np.round( np.exp(result.params),3)[1], \n",
    "            'Coef':np.round( result.params.values,3)[1], \n",
    "            'p-value': np.round( result.pvalues.values,3)[1]  ,\n",
    "             '2.5%': np.round( np.exp(result.conf_int())[0].values,3)[1] , \n",
    "            '97.5%': np.round( np.exp(result.conf_int())[1].values,3)[1],\n",
    "             'LLR p-value' :result.llr_pvalue, \n",
    "            'Pseudo R-squared':result.prsquared\n",
    "           \n",
    "           }\n",
    "\n",
    "    model_odds = model_odds.append(dict, ignore_index = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "1ed01289",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_953b3_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"col_heading level0 col0\" >Feature</th>\n",
       "      <th class=\"col_heading level0 col1\" >OR</th>\n",
       "      <th class=\"col_heading level0 col2\" >Coef</th>\n",
       "      <th class=\"col_heading level0 col3\" >p-value</th>\n",
       "      <th class=\"col_heading level0 col4\" >2.5%</th>\n",
       "      <th class=\"col_heading level0 col5\" >97.5%</th>\n",
       "      <th class=\"col_heading level0 col6\" >LLR p-value</th>\n",
       "      <th class=\"col_heading level0 col7\" >Pseudo R-squared</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td id=\"T_953b3_row0_col0\" class=\"data row0 col0\" >num_elixhauser_2_years_full</td>\n",
       "      <td id=\"T_953b3_row0_col1\" class=\"data row0 col1\" >1.053000</td>\n",
       "      <td id=\"T_953b3_row0_col2\" class=\"data row0 col2\" >0.051000</td>\n",
       "      <td id=\"T_953b3_row0_col3\" class=\"data row0 col3\" >0.000000</td>\n",
       "      <td id=\"T_953b3_row0_col4\" class=\"data row0 col4\" >1.038000</td>\n",
       "      <td id=\"T_953b3_row0_col5\" class=\"data row0 col5\" >1.068000</td>\n",
       "      <td id=\"T_953b3_row0_col6\" class=\"data row0 col6\" >0.000000</td>\n",
       "      <td id=\"T_953b3_row0_col7\" class=\"data row0 col7\" >0.002956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_953b3_row1_col0\" class=\"data row1 col0\" >elix_index</td>\n",
       "      <td id=\"T_953b3_row1_col1\" class=\"data row1 col1\" >1.030000</td>\n",
       "      <td id=\"T_953b3_row1_col2\" class=\"data row1 col2\" >0.029000</td>\n",
       "      <td id=\"T_953b3_row1_col3\" class=\"data row1 col3\" >0.000000</td>\n",
       "      <td id=\"T_953b3_row1_col4\" class=\"data row1 col4\" >1.025000</td>\n",
       "      <td id=\"T_953b3_row1_col5\" class=\"data row1 col5\" >1.034000</td>\n",
       "      <td id=\"T_953b3_row1_col6\" class=\"data row1 col6\" >0.000000</td>\n",
       "      <td id=\"T_953b3_row1_col7\" class=\"data row1 col7\" >0.009596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_953b3_row2_col0\" class=\"data row2 col0\" >num_admits_1_year</td>\n",
       "      <td id=\"T_953b3_row2_col1\" class=\"data row2 col1\" >1.117000</td>\n",
       "      <td id=\"T_953b3_row2_col2\" class=\"data row2 col2\" >0.111000</td>\n",
       "      <td id=\"T_953b3_row2_col3\" class=\"data row2 col3\" >0.000000</td>\n",
       "      <td id=\"T_953b3_row2_col4\" class=\"data row2 col4\" >1.078000</td>\n",
       "      <td id=\"T_953b3_row2_col5\" class=\"data row2 col5\" >1.158000</td>\n",
       "      <td id=\"T_953b3_row2_col6\" class=\"data row2 col6\" >0.000000</td>\n",
       "      <td id=\"T_953b3_row2_col7\" class=\"data row2 col7\" >0.002032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_953b3_row3_col0\" class=\"data row3 col0\" >num_scu_admits_1_year</td>\n",
       "      <td id=\"T_953b3_row3_col1\" class=\"data row3 col1\" >1.212000</td>\n",
       "      <td id=\"T_953b3_row3_col2\" class=\"data row3 col2\" >0.193000</td>\n",
       "      <td id=\"T_953b3_row3_col3\" class=\"data row3 col3\" >0.129000</td>\n",
       "      <td id=\"T_953b3_row3_col4\" class=\"data row3 col4\" >0.945000</td>\n",
       "      <td id=\"T_953b3_row3_col5\" class=\"data row3 col5\" >1.555000</td>\n",
       "      <td id=\"T_953b3_row3_col6\" class=\"data row3 col6\" >0.146053</td>\n",
       "      <td id=\"T_953b3_row3_col7\" class=\"data row3 col7\" >0.000126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_953b3_row4_col0\" class=\"data row4 col0\" >num_procs_dad_1_year</td>\n",
       "      <td id=\"T_953b3_row4_col1\" class=\"data row4 col1\" >1.040000</td>\n",
       "      <td id=\"T_953b3_row4_col2\" class=\"data row4 col2\" >0.039000</td>\n",
       "      <td id=\"T_953b3_row4_col3\" class=\"data row4 col3\" >0.006000</td>\n",
       "      <td id=\"T_953b3_row4_col4\" class=\"data row4 col4\" >1.011000</td>\n",
       "      <td id=\"T_953b3_row4_col5\" class=\"data row4 col5\" >1.069000</td>\n",
       "      <td id=\"T_953b3_row4_col6\" class=\"data row4 col6\" >0.008808</td>\n",
       "      <td id=\"T_953b3_row4_col7\" class=\"data row4 col7\" >0.000409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_953b3_row5_col0\" class=\"data row5 col0\" >num_procs_nacrs_1_year</td>\n",
       "      <td id=\"T_953b3_row5_col1\" class=\"data row5 col1\" >1.001000</td>\n",
       "      <td id=\"T_953b3_row5_col2\" class=\"data row5 col2\" >0.001000</td>\n",
       "      <td id=\"T_953b3_row5_col3\" class=\"data row5 col3\" >0.469000</td>\n",
       "      <td id=\"T_953b3_row5_col4\" class=\"data row5 col4\" >0.999000</td>\n",
       "      <td id=\"T_953b3_row5_col5\" class=\"data row5 col5\" >1.002000</td>\n",
       "      <td id=\"T_953b3_row5_col6\" class=\"data row5 col6\" >0.478762</td>\n",
       "      <td id=\"T_953b3_row5_col7\" class=\"data row5 col7\" >0.000030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_953b3_row6_col0\" class=\"data row6 col0\" >num_procs_tot_1_year</td>\n",
       "      <td id=\"T_953b3_row6_col1\" class=\"data row6 col1\" >1.001000</td>\n",
       "      <td id=\"T_953b3_row6_col2\" class=\"data row6 col2\" >0.001000</td>\n",
       "      <td id=\"T_953b3_row6_col3\" class=\"data row6 col3\" >0.388000</td>\n",
       "      <td id=\"T_953b3_row6_col4\" class=\"data row6 col4\" >0.999000</td>\n",
       "      <td id=\"T_953b3_row6_col5\" class=\"data row6 col5\" >1.002000</td>\n",
       "      <td id=\"T_953b3_row6_col6\" class=\"data row6 col6\" >0.399449</td>\n",
       "      <td id=\"T_953b3_row6_col7\" class=\"data row6 col7\" >0.000042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_953b3_row7_col0\" class=\"data row7 col0\" >spec_pat_num_age</td>\n",
       "      <td id=\"T_953b3_row7_col1\" class=\"data row7 col1\" >1.038000</td>\n",
       "      <td id=\"T_953b3_row7_col2\" class=\"data row7 col2\" >0.037000</td>\n",
       "      <td id=\"T_953b3_row7_col3\" class=\"data row7 col3\" >0.000000</td>\n",
       "      <td id=\"T_953b3_row7_col4\" class=\"data row7 col4\" >1.034000</td>\n",
       "      <td id=\"T_953b3_row7_col5\" class=\"data row7 col5\" >1.042000</td>\n",
       "      <td id=\"T_953b3_row7_col6\" class=\"data row7 col6\" >0.000000</td>\n",
       "      <td id=\"T_953b3_row7_col7\" class=\"data row7 col7\" >0.021949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_953b3_row8_col0\" class=\"data row8 col0\" >specimen_year_month_collected_con</td>\n",
       "      <td id=\"T_953b3_row8_col1\" class=\"data row8 col1\" >1.081000</td>\n",
       "      <td id=\"T_953b3_row8_col2\" class=\"data row8 col2\" >0.078000</td>\n",
       "      <td id=\"T_953b3_row8_col3\" class=\"data row8 col3\" >0.000000</td>\n",
       "      <td id=\"T_953b3_row8_col4\" class=\"data row8 col4\" >1.068000</td>\n",
       "      <td id=\"T_953b3_row8_col5\" class=\"data row8 col5\" >1.095000</td>\n",
       "      <td id=\"T_953b3_row8_col6\" class=\"data row8 col6\" >0.000000</td>\n",
       "      <td id=\"T_953b3_row8_col7\" class=\"data row8 col7\" >0.008234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_953b3_row9_col0\" class=\"data row9 col0\" >dsl_resident_during_collection</td>\n",
       "      <td id=\"T_953b3_row9_col1\" class=\"data row9 col1\" >0.495000</td>\n",
       "      <td id=\"T_953b3_row9_col2\" class=\"data row9 col2\" >-0.704000</td>\n",
       "      <td id=\"T_953b3_row9_col3\" class=\"data row9 col3\" >0.000000</td>\n",
       "      <td id=\"T_953b3_row9_col4\" class=\"data row9 col4\" >0.452000</td>\n",
       "      <td id=\"T_953b3_row9_col5\" class=\"data row9 col5\" >0.542000</td>\n",
       "      <td id=\"T_953b3_row9_col6\" class=\"data row9 col6\" >0.000000</td>\n",
       "      <td id=\"T_953b3_row9_col7\" class=\"data row9 col7\" >0.014944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_953b3_row10_col0\" class=\"data row10 col0\" >patient_gender_M</td>\n",
       "      <td id=\"T_953b3_row10_col1\" class=\"data row10 col1\" >1.403000</td>\n",
       "      <td id=\"T_953b3_row10_col2\" class=\"data row10 col2\" >0.338000</td>\n",
       "      <td id=\"T_953b3_row10_col3\" class=\"data row10 col3\" >0.000000</td>\n",
       "      <td id=\"T_953b3_row10_col4\" class=\"data row10 col4\" >1.292000</td>\n",
       "      <td id=\"T_953b3_row10_col5\" class=\"data row10 col5\" >1.523000</td>\n",
       "      <td id=\"T_953b3_row10_col6\" class=\"data row10 col6\" >0.000000</td>\n",
       "      <td id=\"T_953b3_row10_col7\" class=\"data row10 col7\" >0.003829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_953b3_row11_col0\" class=\"data row11 col0\" >interp_result_Positive</td>\n",
       "      <td id=\"T_953b3_row11_col1\" class=\"data row11 col1\" >4.246000</td>\n",
       "      <td id=\"T_953b3_row11_col2\" class=\"data row11 col2\" >1.446000</td>\n",
       "      <td id=\"T_953b3_row11_col3\" class=\"data row11 col3\" >0.000000</td>\n",
       "      <td id=\"T_953b3_row11_col4\" class=\"data row11 col4\" >3.888000</td>\n",
       "      <td id=\"T_953b3_row11_col5\" class=\"data row11 col5\" >4.637000</td>\n",
       "      <td id=\"T_953b3_row11_col6\" class=\"data row11 col6\" >0.000000</td>\n",
       "      <td id=\"T_953b3_row11_col7\" class=\"data row11 col7\" >0.055525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_953b3_row12_col0\" class=\"data row12 col0\" >num_elixhauser_2_years_full_cat_0</td>\n",
       "      <td id=\"T_953b3_row12_col1\" class=\"data row12 col1\" >1.052000</td>\n",
       "      <td id=\"T_953b3_row12_col2\" class=\"data row12 col2\" >0.051000</td>\n",
       "      <td id=\"T_953b3_row12_col3\" class=\"data row12 col3\" >0.531000</td>\n",
       "      <td id=\"T_953b3_row12_col4\" class=\"data row12 col4\" >0.898000</td>\n",
       "      <td id=\"T_953b3_row12_col5\" class=\"data row12 col5\" >1.233000</td>\n",
       "      <td id=\"T_953b3_row12_col6\" class=\"data row12 col6\" >0.533045</td>\n",
       "      <td id=\"T_953b3_row12_col7\" class=\"data row12 col7\" >0.000023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_953b3_row13_col0\" class=\"data row13 col0\" >num_elixhauser_2_years_full_cat_1+</td>\n",
       "      <td id=\"T_953b3_row13_col1\" class=\"data row13 col1\" >0.951000</td>\n",
       "      <td id=\"T_953b3_row13_col2\" class=\"data row13 col2\" >-0.051000</td>\n",
       "      <td id=\"T_953b3_row13_col3\" class=\"data row13 col3\" >0.531000</td>\n",
       "      <td id=\"T_953b3_row13_col4\" class=\"data row13 col4\" >0.811000</td>\n",
       "      <td id=\"T_953b3_row13_col5\" class=\"data row13 col5\" >1.114000</td>\n",
       "      <td id=\"T_953b3_row13_col6\" class=\"data row13 col6\" >0.533045</td>\n",
       "      <td id=\"T_953b3_row13_col7\" class=\"data row13 col7\" >0.000023</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f79703b4610>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_odds.style.hide_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a367d52a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fuzzysha/software/miniconda3/envs/hproj/lib/python3.8/site-packages/statsmodels/tsa/tsatools.py:142: FutureWarning: In a future version of pandas all arguments of concat except for the argument 'objs' will be keyword-only\n",
      "  x = pd.concat(x[::order], 1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.315348\n",
      "         Iterations 7\n",
      "#################################################################\n",
      "collected place\n",
      "Odds ratio :\n",
      "const                    0.133657\n",
      "collected_in_ed          3.086610\n",
      "collected_in_hospital    2.330146\n",
      "collected_in_dsl         0.421603\n",
      "dtype: float64\n",
      "\n",
      "Confidence interval :\n",
      "                          0     1\n",
      "const                  0.13  0.14\n",
      "collected_in_ed        2.61  3.64\n",
      "collected_in_hospital  1.92  2.82\n",
      "collected_in_dsl       0.38  0.47\n",
      "#################################################################\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.322026\n",
      "         Iterations 8\n",
      "#################################################################\n",
      "age category\n",
      "Odds ratio :\n",
      "const                    0.14\n",
      "spec_pat_agecat_30-39    0.19\n",
      "spec_pat_agecat_40-49    0.10\n",
      "spec_pat_agecat_50-59    0.20\n",
      "spec_pat_agecat_60-69    0.46\n",
      "spec_pat_agecat_70-79    0.61\n",
      "spec_pat_agecat_18-29    0.32\n",
      "dtype: float64\n",
      "\n",
      "Confidence interval :\n",
      "                          0     1\n",
      "const                  0.13  0.14\n",
      "spec_pat_agecat_30-39  0.07  0.50\n",
      "spec_pat_agecat_40-49  0.04  0.26\n",
      "spec_pat_agecat_50-59  0.14  0.30\n",
      "spec_pat_agecat_60-69  0.38  0.54\n",
      "spec_pat_agecat_70-79  0.55  0.69\n",
      "spec_pat_agecat_18-29  0.08  1.33\n",
      "#################################################################\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.321846\n",
      "         Iterations 6\n",
      "#################################################################\n",
      "symptomatic_during_collection\n",
      "Odds ratio :\n",
      "const                              0.09\n",
      "symptomatic_during_collection_U    2.02\n",
      "symptomatic_during_collection_Y    2.33\n",
      "dtype: float64\n",
      "\n",
      "Confidence interval:\n",
      "                                    0     1\n",
      "const                            0.08  0.09\n",
      "symptomatic_during_collection_U  1.82  2.25\n",
      "symptomatic_during_collection_Y  2.09  2.61\n",
      "#################################################################\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.309157\n",
      "         Iterations 7\n",
      "#################################################################\n",
      "test year and month\n",
      "Odds ratio :\n",
      "const           0.04\n",
      "y_m__2020-10    4.39\n",
      "y_m__2020-11    5.40\n",
      "y_m__2020-12    5.55\n",
      "y_m__2020-3     6.25\n",
      "y_m__2020-4     4.01\n",
      "y_m__2020-5     3.59\n",
      "y_m__2021-3     1.86\n",
      "y_m__2020-7     3.56\n",
      "y_m__2020-8     2.48\n",
      "y_m__2020-9     3.12\n",
      "y_m__2021-1     6.40\n",
      "y_m__2021-2     2.95\n",
      "dtype: float64\n",
      "\n",
      "Confidence interval :\n",
      "                 0     1\n",
      "const         0.03  0.04\n",
      "y_m__2020-10  3.65  5.29\n",
      "y_m__2020-11  4.56  6.40\n",
      "y_m__2020-12  4.75  6.49\n",
      "y_m__2020-3   5.17  7.57\n",
      "y_m__2020-4   3.47  4.64\n",
      "y_m__2020-5   3.04  4.24\n",
      "y_m__2021-3   1.00  3.46\n",
      "y_m__2020-7   2.85  4.44\n",
      "y_m__2020-8   1.82  3.37\n",
      "y_m__2020-9   2.33  4.17\n",
      "y_m__2021-1   5.30  7.73\n",
      "y_m__2021-2   1.99  4.36\n",
      "#################################################################\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fuzzysha/software/miniconda3/envs/hproj/lib/python3.8/site-packages/statsmodels/tsa/tsatools.py:142: FutureWarning: In a future version of pandas all arguments of concat except for the argument 'objs' will be keyword-only\n",
      "  x = pd.concat(x[::order], 1)\n",
      "/Users/fuzzysha/software/miniconda3/envs/hproj/lib/python3.8/site-packages/statsmodels/tsa/tsatools.py:142: FutureWarning: In a future version of pandas all arguments of concat except for the argument 'objs' will be keyword-only\n",
      "  x = pd.concat(x[::order], 1)\n",
      "/Users/fuzzysha/software/miniconda3/envs/hproj/lib/python3.8/site-packages/statsmodels/tsa/tsatools.py:142: FutureWarning: In a future version of pandas all arguments of concat except for the argument 'objs' will be keyword-only\n",
      "  x = pd.concat(x[::order], 1)\n"
     ]
    }
   ],
   "source": [
    "# ODDs for the collected place\n",
    "X=ltc_df_LR_t2_2\n",
    "X=sm.add_constant(X)\n",
    "logit_model=sm.Logit(y,X)\n",
    "result=logit_model.fit()\n",
    "print (\"#################################################################\")\n",
    "print (\"collected place\")\n",
    "print('Odds ratio :')\n",
    "print(np.exp(result.params))\n",
    "print('')\n",
    "print('Confidence interval :')\n",
    "print(np.round( np.exp(result.conf_int()),2))\n",
    "print (\"#################################################################\")\n",
    "\n",
    "\n",
    "\n",
    "# ODDs for age category\n",
    "X=ltc_df_LR_t2_3\n",
    "X=sm.add_constant(X)\n",
    "logit_model=sm.Logit(y,X)\n",
    "result=logit_model.fit()\n",
    "\n",
    "print (\"#################################################################\")\n",
    "print (\"age category\")\n",
    "print('Odds ratio :')\n",
    "print(round(np.exp(result.params),2))\n",
    "print('')\n",
    "print('Confidence interval :')\n",
    "print(np.round( np.exp(result.conf_int()),2))\n",
    "\n",
    "print (\"#################################################################\")\n",
    "\n",
    "# ODDs for symptomatic_during_collection\n",
    "X=ltc_df_LR_t2_4\n",
    "X=sm.add_constant(X)\n",
    "logit_model=sm.Logit(y,X)\n",
    "result=logit_model.fit()\n",
    "\n",
    "print (\"#################################################################\")\n",
    "print (\"symptomatic_during_collection\")\n",
    "print('Odds ratio :')\n",
    "print(round(np.exp(result.params),2))\n",
    "print('')\n",
    "print('Confidence interval:')\n",
    "print(np.round( np.exp(result.conf_int()),2))\n",
    "\n",
    "print (\"#################################################################\")\n",
    "\n",
    "# ODDs for test year and month\n",
    "X=ltc_df_LR_t2_5\n",
    "X=sm.add_constant(X)\n",
    "# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=0)\n",
    "logit_model=sm.Logit(y,X)\n",
    "result=logit_model.fit()\n",
    "print (\"#################################################################\")\n",
    "print (\"test year and month\")\n",
    "print('Odds ratio :')\n",
    "print(round(np.exp(result.params),2))\n",
    "print('')\n",
    "print('Confidence interval :')\n",
    "print(np.round( np.exp(result.conf_int()),2))\n",
    "\n",
    "print (\"#################################################################\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4a764ee",
   "metadata": {},
   "source": [
    "# ODDs ration wtih balanced data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26343b2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# random over sampler\n",
    "scaler_PT = PowerTransformer() \n",
    "over_sampler = RandomOverSampler(random_state=42)\n",
    "\n",
    "# odds for the comorbidiries\n",
    "y=ltc_df_desc_t2_cmd_1['died_within_60_days']\n",
    "cmd=ltc_df_desc_t2_cmd_1.iloc[:,1:]\n",
    "model_odds = pd.DataFrame()#columns= [['Feature','OR','p-value','2.5%', '97.5%', 'LLR p-value','Pseudo R-squared']])\n",
    "\n",
    "i=0\n",
    "for feature in cmd.columns.values:\n",
    "    \n",
    "    X=cmd[feature]\n",
    "    X=sm.add_constant(X)\n",
    "    X_res, y_res = over_sampler.fit_resample(X, y)\n",
    "    logit_model=sm.Logit(y_res,X_res)\n",
    "    result=logit_model.fit()\n",
    "\n",
    "    dict = {'Feature': feature , \n",
    "            'OR': np.round( np.exp(result.params),2)[1], \n",
    "            'Coef':np.round( result.params.values,3)[1], \n",
    "            'p-value': np.round( result.pvalues.values,2)[1]  ,\n",
    "             '2.5%': np.round( np.exp(result.conf_int())[0].values,3)[1] , \n",
    "            '97.5%': np.round( np.exp(result.conf_int())[1].values,3)[1],\n",
    "             'LLR p-value' :result.llr_pvalue, \n",
    "            'Pseudo R-squared':result.prsquared\n",
    "           \n",
    "           }\n",
    "\n",
    "    model_odds = model_odds.append(dict, ignore_index = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "2d8e2108",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>Feature</th>\n",
       "      <th>OR</th>\n",
       "      <th>p-value</th>\n",
       "      <th>2.5%</th>\n",
       "      <th>97.5%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>chf_2_years_full_1</td>\n",
       "      <td>1.62</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.556</td>\n",
       "      <td>1.697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>htn_unc_2_years_full_1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.960</td>\n",
       "      <td>1.033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>cancer_mets_2_years_full_1</td>\n",
       "      <td>1.91</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.669</td>\n",
       "      <td>2.192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>fluid_elec_dis_2_years_full_1</td>\n",
       "      <td>1.48</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.416</td>\n",
       "      <td>1.537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>card_arrh_2_years_full_1</td>\n",
       "      <td>1.36</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.308</td>\n",
       "      <td>1.423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>valv_dis_2_years_full_1</td>\n",
       "      <td>1.42</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.281</td>\n",
       "      <td>1.578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>pcd_2_years_full_1</td>\n",
       "      <td>1.65</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.512</td>\n",
       "      <td>1.803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>pvd_2_years_full_1</td>\n",
       "      <td>1.32</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.238</td>\n",
       "      <td>1.398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>htn_c_2_years_full_1</td>\n",
       "      <td>1.27</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.062</td>\n",
       "      <td>1.524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>paral_2_years_full_1</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.602</td>\n",
       "      <td>0.721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>oth_neur_dis_2_years_full_1</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.953</td>\n",
       "      <td>1.027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>cpd_2_years_full_1</td>\n",
       "      <td>1.33</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.268</td>\n",
       "      <td>1.384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>diab_2_years_full_1</td>\n",
       "      <td>1.12</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.079</td>\n",
       "      <td>1.168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>hypothyroidism_2_years_full_1</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.940</td>\n",
       "      <td>1.049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>ren_fail_2_years_full_1</td>\n",
       "      <td>1.30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.236</td>\n",
       "      <td>1.370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>liver_dis_2_years_full_1</td>\n",
       "      <td>1.15</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.032</td>\n",
       "      <td>1.287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>pep_ulc_exc_bld_2_years_full_1</td>\n",
       "      <td>1.58</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.370</td>\n",
       "      <td>1.818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>aids_hiv_2_years_full_1</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.138</td>\n",
       "      <td>0.609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>lymph_2_years_full_1</td>\n",
       "      <td>1.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.174</td>\n",
       "      <td>1.665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>tumour_no_mets_2_years_full_1</td>\n",
       "      <td>1.41</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.331</td>\n",
       "      <td>1.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>rheum_col_vasc_dis_2_years_full_1</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.912</td>\n",
       "      <td>0.990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>coag_2_years_full_1</td>\n",
       "      <td>1.37</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.250</td>\n",
       "      <td>1.510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>obes_2_years_full_1</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.648</td>\n",
       "      <td>0.792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>wt_loss_2_years_full_1</td>\n",
       "      <td>1.27</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.173</td>\n",
       "      <td>1.368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>anemia_2_years_full_1</td>\n",
       "      <td>1.37</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.299</td>\n",
       "      <td>1.455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>alc_abuse_2_years_full_1</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.652</td>\n",
       "      <td>0.780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>drug_abuse_2_years_full_1</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.587</td>\n",
       "      <td>0.710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>psych_2_years_full_1</td>\n",
       "      <td>1.08</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.040</td>\n",
       "      <td>1.130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>depress_2_years_full_1</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.812</td>\n",
       "      <td>0.874</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Feature    OR  p-value   2.5%  97.5%\n",
       "0                  chf_2_years_full_1  1.62     0.00  1.556  1.697\n",
       "1              htn_unc_2_years_full_1  1.00     0.82  0.960  1.033\n",
       "2          cancer_mets_2_years_full_1  1.91     0.00  1.669  2.192\n",
       "3       fluid_elec_dis_2_years_full_1  1.48     0.00  1.416  1.537\n",
       "4            card_arrh_2_years_full_1  1.36     0.00  1.308  1.423\n",
       "5             valv_dis_2_years_full_1  1.42     0.00  1.281  1.578\n",
       "6                  pcd_2_years_full_1  1.65     0.00  1.512  1.803\n",
       "7                  pvd_2_years_full_1  1.32     0.00  1.238  1.398\n",
       "8                htn_c_2_years_full_1  1.27     0.01  1.062  1.524\n",
       "9                paral_2_years_full_1  0.66     0.00  0.602  0.721\n",
       "10        oth_neur_dis_2_years_full_1  0.99     0.58  0.953  1.027\n",
       "11                 cpd_2_years_full_1  1.33     0.00  1.268  1.384\n",
       "12                diab_2_years_full_1  1.12     0.00  1.079  1.168\n",
       "13      hypothyroidism_2_years_full_1  0.99     0.80  0.940  1.049\n",
       "14            ren_fail_2_years_full_1  1.30     0.00  1.236  1.370\n",
       "15           liver_dis_2_years_full_1  1.15     0.01  1.032  1.287\n",
       "16     pep_ulc_exc_bld_2_years_full_1  1.58     0.00  1.370  1.818\n",
       "17            aids_hiv_2_years_full_1  0.29     0.00  0.138  0.609\n",
       "18               lymph_2_years_full_1  1.40     0.00  1.174  1.665\n",
       "19      tumour_no_mets_2_years_full_1  1.41     0.00  1.331  1.500\n",
       "20  rheum_col_vasc_dis_2_years_full_1  0.95     0.01  0.912  0.990\n",
       "21                coag_2_years_full_1  1.37     0.00  1.250  1.510\n",
       "22                obes_2_years_full_1  0.72     0.00  0.648  0.792\n",
       "23             wt_loss_2_years_full_1  1.27     0.00  1.173  1.368\n",
       "24              anemia_2_years_full_1  1.37     0.00  1.299  1.455\n",
       "25           alc_abuse_2_years_full_1  0.71     0.00  0.652  0.780\n",
       "26          drug_abuse_2_years_full_1  0.65     0.00  0.587  0.710\n",
       "27               psych_2_years_full_1  1.08     0.00  1.040  1.130\n",
       "28             depress_2_years_full_1  0.84     0.00  0.812  0.874"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_odds[['Feature','OR','p-value','2.5%','97.5%']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cfb8cd19",
   "metadata": {},
   "outputs": [],
   "source": [
    "# odds for the categorical values\n",
    "\n",
    "y=data_final['died_within_60_days']\n",
    "model_odds = pd.DataFrame()#columns= [['Feature','OR','p-value','2.5%', '97.5%', 'LLR p-value','Pseudo R-squared']])\n",
    "\n",
    "i=0\n",
    "for feature in ltc_df_LR_t2_1.columns.values:\n",
    "#     X=pd.DataFrame(ltc_df_LR_t2_1[feature])\n",
    "    X=ltc_df_LR_t2_1[feature]\n",
    "    \n",
    "    print(X.shape)\n",
    "#     X = pd.DataFrame(scaler_PT.fit_transform(X), columns=X.columns)\n",
    "    X=sm.add_constant(X)\n",
    "#     X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=0)\n",
    "    X_res, y_res = over_sampler.fit_resample(X, y)\n",
    "    logit_model=sm.Logit(y_res,X_res)\n",
    "    result=logit_model.fit()\n",
    "    dict = {'Feature': feature , \n",
    "            'OR': np.round( np.exp(result.params.values),3)[1], \n",
    "            'p-value': np.round( result.pvalues.values,3)[1]  ,\n",
    "             '2.5%': np.round( np.exp(result.conf_int())[0].values,3)[1] , \n",
    "            '97.5%': np.round( np.exp(result.conf_int())[1].values,3)[1],\n",
    "             'LLR p-value' :result.llr_pvalue, \n",
    "            'Pseudo R-squared':result.prsquared\n",
    "           \n",
    "           }\n",
    "\n",
    "    model_odds = model_odds.append(dict, ignore_index = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "381f2da3",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th class=\"col_heading level0 col0\" >Feature</th>\n",
       "      <th class=\"col_heading level0 col1\" >OR</th>\n",
       "      <th class=\"col_heading level0 col2\" >p-value</th>\n",
       "      <th class=\"col_heading level0 col3\" >2.5%</th>\n",
       "      <th class=\"col_heading level0 col4\" >97.5%</th>\n",
       "      <th class=\"col_heading level0 col5\" >LLR p-value</th>\n",
       "      <th class=\"col_heading level0 col6\" >Pseudo R-squared</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td id=\"T_b5a91_row0_col0\" class=\"data row0 col0\" >num_elixhauser_2_years_full</td>\n",
       "      <td id=\"T_b5a91_row0_col1\" class=\"data row0 col1\" >1.051000</td>\n",
       "      <td id=\"T_b5a91_row0_col2\" class=\"data row0 col2\" >0.000000</td>\n",
       "      <td id=\"T_b5a91_row0_col3\" class=\"data row0 col3\" >1.044000</td>\n",
       "      <td id=\"T_b5a91_row0_col4\" class=\"data row0 col4\" >1.058000</td>\n",
       "      <td id=\"T_b5a91_row0_col5\" class=\"data row0 col5\" >0.000000</td>\n",
       "      <td id=\"T_b5a91_row0_col6\" class=\"data row0 col6\" >0.003671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_b5a91_row1_col0\" class=\"data row1 col0\" >elix_index</td>\n",
       "      <td id=\"T_b5a91_row1_col1\" class=\"data row1 col1\" >1.030000</td>\n",
       "      <td id=\"T_b5a91_row1_col2\" class=\"data row1 col2\" >0.000000</td>\n",
       "      <td id=\"T_b5a91_row1_col3\" class=\"data row1 col3\" >1.028000</td>\n",
       "      <td id=\"T_b5a91_row1_col4\" class=\"data row1 col4\" >1.032000</td>\n",
       "      <td id=\"T_b5a91_row1_col5\" class=\"data row1 col5\" >0.000000</td>\n",
       "      <td id=\"T_b5a91_row1_col6\" class=\"data row1 col6\" >0.012059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_b5a91_row2_col0\" class=\"data row2 col0\" >num_admits_1_year</td>\n",
       "      <td id=\"T_b5a91_row2_col1\" class=\"data row2 col1\" >1.125000</td>\n",
       "      <td id=\"T_b5a91_row2_col2\" class=\"data row2 col2\" >0.000000</td>\n",
       "      <td id=\"T_b5a91_row2_col3\" class=\"data row2 col3\" >1.105000</td>\n",
       "      <td id=\"T_b5a91_row2_col4\" class=\"data row2 col4\" >1.145000</td>\n",
       "      <td id=\"T_b5a91_row2_col5\" class=\"data row2 col5\" >0.000000</td>\n",
       "      <td id=\"T_b5a91_row2_col6\" class=\"data row2 col6\" >0.002762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_b5a91_row3_col0\" class=\"data row3 col0\" >num_scu_admits_1_year</td>\n",
       "      <td id=\"T_b5a91_row3_col1\" class=\"data row3 col1\" >1.218000</td>\n",
       "      <td id=\"T_b5a91_row3_col2\" class=\"data row3 col2\" >0.002000</td>\n",
       "      <td id=\"T_b5a91_row3_col3\" class=\"data row3 col3\" >1.074000</td>\n",
       "      <td id=\"T_b5a91_row3_col4\" class=\"data row3 col4\" >1.381000</td>\n",
       "      <td id=\"T_b5a91_row3_col5\" class=\"data row3 col5\" >0.001928</td>\n",
       "      <td id=\"T_b5a91_row3_col6\" class=\"data row3 col6\" >0.000151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_b5a91_row4_col0\" class=\"data row4 col0\" >num_procs_dad_1_year</td>\n",
       "      <td id=\"T_b5a91_row4_col1\" class=\"data row4 col1\" >1.036000</td>\n",
       "      <td id=\"T_b5a91_row4_col2\" class=\"data row4 col2\" >0.000000</td>\n",
       "      <td id=\"T_b5a91_row4_col3\" class=\"data row4 col3\" >1.021000</td>\n",
       "      <td id=\"T_b5a91_row4_col4\" class=\"data row4 col4\" >1.050000</td>\n",
       "      <td id=\"T_b5a91_row4_col5\" class=\"data row4 col5\" >0.000001</td>\n",
       "      <td id=\"T_b5a91_row4_col6\" class=\"data row4 col6\" >0.000387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_b5a91_row5_col0\" class=\"data row5 col0\" >num_procs_nacrs_1_year</td>\n",
       "      <td id=\"T_b5a91_row5_col1\" class=\"data row5 col1\" >1.000000</td>\n",
       "      <td id=\"T_b5a91_row5_col2\" class=\"data row5 col2\" >0.552000</td>\n",
       "      <td id=\"T_b5a91_row5_col3\" class=\"data row5 col3\" >0.999000</td>\n",
       "      <td id=\"T_b5a91_row5_col4\" class=\"data row5 col4\" >1.001000</td>\n",
       "      <td id=\"T_b5a91_row5_col5\" class=\"data row5 col5\" >0.551801</td>\n",
       "      <td id=\"T_b5a91_row5_col6\" class=\"data row5 col6\" >0.000006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_b5a91_row6_col0\" class=\"data row6 col0\" >num_procs_tot_1_year</td>\n",
       "      <td id=\"T_b5a91_row6_col1\" class=\"data row6 col1\" >1.000000</td>\n",
       "      <td id=\"T_b5a91_row6_col2\" class=\"data row6 col2\" >0.384000</td>\n",
       "      <td id=\"T_b5a91_row6_col3\" class=\"data row6 col3\" >1.000000</td>\n",
       "      <td id=\"T_b5a91_row6_col4\" class=\"data row6 col4\" >1.001000</td>\n",
       "      <td id=\"T_b5a91_row6_col5\" class=\"data row6 col5\" >0.383593</td>\n",
       "      <td id=\"T_b5a91_row6_col6\" class=\"data row6 col6\" >0.000012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_b5a91_row7_col0\" class=\"data row7 col0\" >spec_pat_num_age</td>\n",
       "      <td id=\"T_b5a91_row7_col1\" class=\"data row7 col1\" >1.037000</td>\n",
       "      <td id=\"T_b5a91_row7_col2\" class=\"data row7 col2\" >0.000000</td>\n",
       "      <td id=\"T_b5a91_row7_col3\" class=\"data row7 col3\" >1.036000</td>\n",
       "      <td id=\"T_b5a91_row7_col4\" class=\"data row7 col4\" >1.039000</td>\n",
       "      <td id=\"T_b5a91_row7_col5\" class=\"data row7 col5\" >0.000000</td>\n",
       "      <td id=\"T_b5a91_row7_col6\" class=\"data row7 col6\" >0.029138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_b5a91_row8_col0\" class=\"data row8 col0\" >specimen_year_month_collected_con</td>\n",
       "      <td id=\"T_b5a91_row8_col1\" class=\"data row8 col1\" >1.073000</td>\n",
       "      <td id=\"T_b5a91_row8_col2\" class=\"data row8 col2\" >0.000000</td>\n",
       "      <td id=\"T_b5a91_row8_col3\" class=\"data row8 col3\" >1.067000</td>\n",
       "      <td id=\"T_b5a91_row8_col4\" class=\"data row8 col4\" >1.079000</td>\n",
       "      <td id=\"T_b5a91_row8_col5\" class=\"data row8 col5\" >0.000000</td>\n",
       "      <td id=\"T_b5a91_row8_col6\" class=\"data row8 col6\" >0.009430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_b5a91_row9_col0\" class=\"data row9 col0\" >dsl_resident_during_collection</td>\n",
       "      <td id=\"T_b5a91_row9_col1\" class=\"data row9 col1\" >0.496000</td>\n",
       "      <td id=\"T_b5a91_row9_col2\" class=\"data row9 col2\" >0.000000</td>\n",
       "      <td id=\"T_b5a91_row9_col3\" class=\"data row9 col3\" >0.477000</td>\n",
       "      <td id=\"T_b5a91_row9_col4\" class=\"data row9 col4\" >0.516000</td>\n",
       "      <td id=\"T_b5a91_row9_col5\" class=\"data row9 col5\" >0.000000</td>\n",
       "      <td id=\"T_b5a91_row9_col6\" class=\"data row9 col6\" >0.019785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_b5a91_row10_col0\" class=\"data row10 col0\" >patient_gender_M</td>\n",
       "      <td id=\"T_b5a91_row10_col1\" class=\"data row10 col1\" >1.414000</td>\n",
       "      <td id=\"T_b5a91_row10_col2\" class=\"data row10 col2\" >0.000000</td>\n",
       "      <td id=\"T_b5a91_row10_col3\" class=\"data row10 col3\" >1.362000</td>\n",
       "      <td id=\"T_b5a91_row10_col4\" class=\"data row10 col4\" >1.468000</td>\n",
       "      <td id=\"T_b5a91_row10_col5\" class=\"data row10 col5\" >0.000000</td>\n",
       "      <td id=\"T_b5a91_row10_col6\" class=\"data row10 col6\" >0.005159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_b5a91_row11_col0\" class=\"data row11 col0\" >interp_result_Positive</td>\n",
       "      <td id=\"T_b5a91_row11_col1\" class=\"data row11 col1\" >4.242000</td>\n",
       "      <td id=\"T_b5a91_row11_col2\" class=\"data row11 col2\" >0.000000</td>\n",
       "      <td id=\"T_b5a91_row11_col3\" class=\"data row11 col3\" >4.047000</td>\n",
       "      <td id=\"T_b5a91_row11_col4\" class=\"data row11 col4\" >4.445000</td>\n",
       "      <td id=\"T_b5a91_row11_col5\" class=\"data row11 col5\" >0.000000</td>\n",
       "      <td id=\"T_b5a91_row11_col6\" class=\"data row11 col6\" >0.064237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_b5a91_row12_col0\" class=\"data row12 col0\" >num_elixhauser_2_years_full_cat_0</td>\n",
       "      <td id=\"T_b5a91_row12_col1\" class=\"data row12 col1\" >1.075000</td>\n",
       "      <td id=\"T_b5a91_row12_col2\" class=\"data row12 col2\" >0.049000</td>\n",
       "      <td id=\"T_b5a91_row12_col3\" class=\"data row12 col3\" >1.000000</td>\n",
       "      <td id=\"T_b5a91_row12_col4\" class=\"data row12 col4\" >1.155000</td>\n",
       "      <td id=\"T_b5a91_row12_col5\" class=\"data row12 col5\" >0.048535</td>\n",
       "      <td id=\"T_b5a91_row12_col6\" class=\"data row12 col6\" >0.000061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_b5a91_row13_col0\" class=\"data row13 col0\" >num_elixhauser_2_years_full_cat_1+</td>\n",
       "      <td id=\"T_b5a91_row13_col1\" class=\"data row13 col1\" >0.930000</td>\n",
       "      <td id=\"T_b5a91_row13_col2\" class=\"data row13 col2\" >0.049000</td>\n",
       "      <td id=\"T_b5a91_row13_col3\" class=\"data row13 col3\" >0.866000</td>\n",
       "      <td id=\"T_b5a91_row13_col4\" class=\"data row13 col4\" >1.000000</td>\n",
       "      <td id=\"T_b5a91_row13_col5\" class=\"data row13 col5\" >0.048535</td>\n",
       "      <td id=\"T_b5a91_row13_col6\" class=\"data row13 col6\" >0.000061</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f7971eb1a00>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_odds.style.hide_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c67ecfb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=ltc_df_LR_t2_2\n",
    "X=sm.add_constant(X)\n",
    "X_res, y_res = over_sampler.fit_resample(X, y)\n",
    "logit_model=sm.Logit(y_res,X_res)\n",
    "result_collected_place=logit_model.fit()\n",
    "\n",
    "\n",
    "X=ltc_df_LR_t2_3\n",
    "X=sm.add_constant(X)\n",
    "X_res, y_res = over_sampler.fit_resample(X, y)\n",
    "logit_model=sm.Logit(y_res,X_res)\n",
    "result_age=logit_model.fit()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "X=ltc_df_LR_t2_4\n",
    "X=sm.add_constant(X)\n",
    "# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=0)\n",
    "X_res, y_res = over_sampler.fit_resample(X, y)\n",
    "logit_model=sm.Logit(y_res,X_res)\n",
    "result_symptomatic=logit_model.fit()\n",
    "\n",
    "\n",
    "\n",
    "X=ltc_df_LR_t2_5\n",
    "X=sm.add_constant(X)\n",
    "# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=0)\n",
    "X_res, y_res = over_sampler.fit_resample(X, y)\n",
    "logit_model=sm.Logit(y_res,X_res)\n",
    "result_y_m=logit_model.fit()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "d3056994",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#################################################################\n",
      "collected place\n",
      "Odds ratio :\n",
      "const                    1.179\n",
      "collected_in_ed          3.090\n",
      "collected_in_hospital    2.507\n",
      "collected_in_dsl         0.426\n",
      "dtype: float64\n",
      "#################################################################\n",
      "#################################################################\n",
      "Age category\n",
      "Odds ratio :\n",
      "const                    1.221\n",
      "spec_pat_agecat_30-39    0.231\n",
      "spec_pat_agecat_40-49    0.089\n",
      "spec_pat_agecat_50-59    0.205\n",
      "spec_pat_agecat_60-69    0.468\n",
      "spec_pat_agecat_70-79    0.621\n",
      "spec_pat_agecat_18-29    0.237\n",
      "dtype: float64\n",
      "#################################################################\n",
      "#################################################################\n",
      "symptomatic_during_collection\n",
      "Odds ratio :\n",
      "const                              0.789\n",
      "symptomatic_during_collection_U    1.987\n",
      "symptomatic_during_collection_Y    2.375\n",
      "dtype: float64\n",
      "#################################################################\n",
      "#################################################################\n",
      "Year and month of the test collection\n",
      "Odds ratio :\n",
      "const           0.320\n",
      "y_m__2020-10    4.413\n",
      "y_m__2020-11    5.644\n",
      "y_m__2020-12    5.766\n",
      "y_m__2020-3     6.424\n",
      "y_m__2020-4     4.133\n",
      "y_m__2020-5     3.686\n",
      "y_m__2021-3     1.939\n",
      "y_m__2020-7     3.513\n",
      "y_m__2020-8     2.337\n",
      "y_m__2020-9     3.177\n",
      "y_m__2021-1     6.219\n",
      "y_m__2021-2     3.269\n",
      "dtype: float64\n",
      "#################################################################\n"
     ]
    }
   ],
   "source": [
    "print (\"#################################################################\")\n",
    "print (\"collected place\")\n",
    "print('Odds ratio :')\n",
    "print(round(np.exp(result_collected_place.params),3))\n",
    "print (\"#################################################################\")\n",
    "\n",
    "\n",
    "print (\"#################################################################\")\n",
    "print (\"Age category\")\n",
    "print('Odds ratio :')\n",
    "print(round(np.exp(result_age.params),3))\n",
    "print (\"#################################################################\")\n",
    "\n",
    "print (\"#################################################################\")\n",
    "print (\"symptomatic_during_collection\")\n",
    "print('Odds ratio :')\n",
    "print(round(np.exp(result_symptomatic.params),3))\n",
    "print (\"#################################################################\")\n",
    "\n",
    "\n",
    "print (\"#################################################################\")\n",
    "print (\"Year and month of the test collection\")\n",
    "print('Odds ratio :')\n",
    "print(round(np.exp(result_y_m.params),3))\n",
    "print (\"#################################################################\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "952bcece",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_final.to_hdf(\"data_final.h5\", key='data_final')"
   ]
  }
 ],
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