# pip install pycaret from pandas.api.types import CategoricalDtype import pandas as pd import jinja2 from pycaret.classification import * import imblearn as im import sklearn import gradio as gr import numpy as np import io import pickle import requests import urllib.request import shutil # url = 'https://raw.githubusercontent.com/fmegahed/tavr_paper/main/data/example_data2.csv' # download = requests.get(url).content ex_data =pd.read_csv('example_data2.csv') ex_data = ex_data.to_numpy() ex_data = ex_data.tolist() def predict(age, female, race, elective, aweekend, zipinc_qrtl, hosp_region, hosp_division, hosp_locteach, hosp_bedsize, h_contrl, pay, anemia, atrial_fibrillation, cancer, cardiac_arrhythmias, carotid_artery_disease, chronic_kidney_disease, chronic_pulmonary_disease, coagulopathy, depression, diabetes_mellitus, drug_abuse, dyslipidemia, endocarditis, family_history, fluid_and_electrolyte_disorder, heart_failure, hypertension, known_cad, liver_disease, obesity, peripheral_vascular_disease, prior_cabg, prior_icd, prior_mi, prior_pci, prior_ppm, prior_tia_stroke, pulmonary_circulation_disorder, smoker, valvular_disease, weight_loss, endovascular_tavr, transapical_tavr): df = pd.DataFrame.from_dict({ 'age': [age], 'female': [female], 'race': [race], 'elective': elective, 'aweekend': [aweekend], 'zipinc_qrtl': [zipinc_qrtl], 'hosp_region': [hosp_region], 'hosp_division': [hosp_division], 'hosp_locteach': [hosp_locteach], 'hosp_bedsize': [hosp_bedsize], 'h_contrl': [h_contrl], 'pay': [pay], 'anemia': [anemia], 'atrial_fibrillation': [atrial_fibrillation], 'cancer': [cancer], 'cardiac_arrhythmias': [cardiac_arrhythmias], 'carotid_artery_disease': [carotid_artery_disease], 'chronic_kidney_disease': [chronic_kidney_disease], 'chronic_pulmonary_disease': [chronic_pulmonary_disease], 'coagulopathy': [coagulopathy], 'depression': [depression], 'diabetes_mellitus': [diabetes_mellitus], 'drug_abuse': [drug_abuse], 'dyslipidemia': [dyslipidemia], 'endocarditis': [endocarditis], 'family_history': [family_history], 'fluid_and_electrolyte_disorder': [fluid_and_electrolyte_disorder], 'heart_failure': [heart_failure], 'hypertension': [hypertension], 'known_cad': [known_cad], 'liver_disease': [liver_disease], 'obesity': [obesity], 'peripheral_vascular_disease': [peripheral_vascular_disease], 'prior_cabg': [prior_cabg], 'prior_icd': [prior_icd], 'prior_mi': [prior_mi], 'prior_pci': [prior_pci], 'prior_ppm': [prior_ppm], 'prior_tia_stroke': [prior_tia_stroke], 'pulmonary_circulation_disorder': [pulmonary_circulation_disorder], 'smoker': [smoker], 'valvular_disease': [valvular_disease], 'weight_loss': [weight_loss], 'endovascular_tavr': [endovascular_tavr], 'transapical_tavr': [transapical_tavr] }) df.loc[:, df.dtypes == 'object'] =\ df.select_dtypes(['object'])\ .apply(lambda x: x.astype('category')) # converting ordinal column to ordinal ordinal_cat = CategoricalDtype(categories = ['FirstQ', 'SecondQ', 'ThirdQ', 'FourthQ'], ordered = True) df.zipinc_qrtl = df.zipinc_qrtl.astype(ordinal_cat) with urllib.request.urlopen('https://github.com/fmegahed/tavr_paper/blob/main/data/final_model.pkl?raw=true') as response, open('final_model.pkl', 'wb') as out_file: shutil.copyfileobj(response, out_file) model = load_model('final_model') pred = predict_model(model, df, raw_score=True) return {'Death %': round(100*pred['Score_Yes'][0], 2), 'Survival %': round(100*pred['Score_No'][0], 2), 'Predicting Death Outcome:': pred['Label'][0]} # Defining the containers for each input age = gr.inputs.Slider(minimum=18, maximum=100, default=60, label="Age") female = gr.inputs.Dropdown(choices=["Female", "Male"],label = 'Sex') race = gr.inputs.Dropdown(choices=['Asian or Pacific Islander', 'Black', 'Hispanic', 'Native American', 'White', 'Other'], label = 'Race') elective = gr.inputs.Radio(choices=['Elective', 'NonElective'], label = 'Elective') aweekend = gr.inputs.Radio(choices=["No", "Yes"], label = 'Weekend') zipinc_qrtl = gr.inputs.Radio(choices=['FirstQ', 'SecondQ', 'ThirdQ', 'FourthQ'], label = 'Zip Income Quartile') hosp_region = gr.inputs.Radio(choices=['Midwest', 'Northeast', 'South', 'West'], label = 'Hospital Region') hosp_division = gr.inputs.Radio(choices=['New England', 'Middle Atlantic', 'East North Central', 'West North Central', 'South Atlantic', 'East South Central', 'West South Central', 'Mountain', 'Pacific'], label = 'Hospital Division') hosp_locteach = gr.inputs.Radio(choices=['Urban teaching', 'Urban nonteaching', 'Rural'], label= 'Hospital Location/Teaching') hosp_bedsize = gr.inputs.Radio(choices=['Small', 'Medium', 'Large'], label= 'Hospital Bedsize') h_contrl = gr.inputs.Radio(choices= ['Government_nonfederal', 'Private_invest_own', 'Private_not_profit'], label = 'Hospital Control') pay = gr.inputs.Dropdown(choices= ['Private insurance', 'Medicare', 'Medicaid', 'Self-pay', 'No charge', 'Other'], label = 'Payee') anemia = gr.inputs.Radio(choices=["No", "Yes"], label = 'Anemia') atrial_fibrillation = gr.inputs.Radio(choices=["No", "Yes"], label = 'Atrial Fibrillation') cancer = gr.inputs.Radio(choices=["No", "Yes"], label = 'Cancer') cardiac_arrhythmias = gr.inputs.Radio(choices=["No", "Yes"], label = 'Cardiac Arrhythmias') carotid_artery_disease = gr.inputs.Radio(choices=["No", "Yes"], label = 'Carotid Artery Disease') chronic_kidney_disease = gr.inputs.Radio(choices=["No", "Yes"], label = 'Chronic Kidney Disease') chronic_pulmonary_disease = gr.inputs.Radio(choices=["No", "Yes"], label = 'Chronic Pulmonary Disease') coagulopathy = gr.inputs.Radio(choices=["No", "Yes"], label = 'Coagulopathy') depression = gr.inputs.Radio(choices=["No", "Yes"], label = 'Depression') diabetes_mellitus = gr.inputs.Radio(choices=["No", "Yes"], label = 'Diabetes Mellitus') drug_abuse = gr.inputs.Radio(choices=["No", "Yes"], label = 'Drug Abuse') dyslipidemia = gr.inputs.Radio(choices=["No", "Yes"], label = 'Dyslipidemia') endocarditis = gr.inputs.Radio(choices=["No", "Yes"], label = 'Endocarditis') family_history = gr.inputs.Radio(choices=["No", "Yes"], label = 'Family History') fluid_and_electrolyte_disorder = gr.inputs.Radio(choices=["No", "Yes"], label = 'Fluid and Electrolyte Disorder') heart_failure = gr.inputs.Radio(choices=["No", "Yes"], label = 'Heart Failure') hypertension = gr.inputs.Radio(choices=["No", "Yes"], label = 'Hypertension') known_cad = gr.inputs.Radio(choices=["No", "Yes"], label = 'Known CAD') liver_disease = gr.inputs.Radio(choices=["No", "Yes"], label = 'Liver Disease') obesity = gr.inputs.Radio(choices=["No", "Yes"], label = 'Obesity') peripheral_vascular_disease = gr.inputs.Radio(choices=["No", "Yes"], label = 'Peripheral Vascular Disease') prior_cabg = gr.inputs.Radio(choices=["No", "Yes"], label = 'Prior CABG') prior_icd = gr.inputs.Radio(choices=["No", "Yes"], label = 'Prior ICD') prior_mi = gr.inputs.Radio(choices=["No", "Yes"], label = 'Prior MI') prior_pci = gr.inputs.Radio(choices=["No", "Yes"], label = 'Prior PCI') prior_ppm = gr.inputs.Radio(choices=["No", "Yes"], label = 'Prior PPM') prior_tia_stroke = gr.inputs.Radio(choices=["No", "Yes"], label = 'Prior TIA Stroke') pulmonary_circulation_disorder = gr.inputs.Radio(choices=["No", "Yes"], label = 'Pulmonary Circulation Disorder') smoker = gr.inputs.Radio(choices=["No", "Yes"], label = 'Smoker') valvular_disease = gr.inputs.Radio(choices=["No", "Yes"], label = 'Valvular Disease') weight_loss = gr.inputs.Radio(choices=["No", "Yes"], label = 'Weight Loss') endovascular_tavr = gr.inputs.Radio(choices=["No", "Yes"], label = 'Endovascular TAVR') transapical_tavr = gr.inputs.Radio(choices=["No", "Yes"], label = 'Transapical TAVR', default= 'Yes') # Defining and launching the interface iface = gr.Interface( fn = predict, inputs = [age, female, race, elective, aweekend, zipinc_qrtl, hosp_region, hosp_division, hosp_locteach, hosp_bedsize, h_contrl, pay, anemia, atrial_fibrillation, cancer, cardiac_arrhythmias, carotid_artery_disease, chronic_kidney_disease, chronic_pulmonary_disease, coagulopathy, depression, diabetes_mellitus, drug_abuse, dyslipidemia, endocarditis, family_history, fluid_and_electrolyte_disorder, heart_failure, hypertension, known_cad, liver_disease, obesity, peripheral_vascular_disease, prior_cabg, prior_icd, prior_mi, prior_pci, prior_ppm, prior_tia_stroke, pulmonary_circulation_disorder, smoker, valvular_disease, weight_loss, endovascular_tavr, transapical_tavr], outputs = 'text', live=True, title = "Predicting In-Hospital Mortality After TAVR Using Preoperative Variables and Penalized Logistic Regression", description = "The app below utilizes the finalized logistic regression model with an l2 penalty based on the manuscript by Alhwiti, Aldrugh, and Megahed. The manuscript is under review at Scientific Reports. The data used for model building is all TAVR procedures between 2012 and 2019, as reported in the HCUP NIS database.

The app's purpose is to provide evidence-based clinical support for interventional cardiology.", css = 'https://bootswatch.com/5/journal/bootstrap.css') iface.launch()