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| # 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 | |
| inputs = [ | |
| gr.Slider(minimum=18, maximum=100, value=80, label="Age"), | |
| gr.Dropdown(choices=["Female", "Male"], value="Female", label="Sex"), | |
| gr.Dropdown(choices=['Asian or Pacific Islander', 'Black', 'Hispanic', 'Native American', 'White', 'Other'], value='White', label='Race'), | |
| gr.Radio(choices=['Elective', 'NonElective'], value='Elective', label='Elective'), | |
| gr.Radio(choices=["No", "Yes"], value="No", label='Weekend'), | |
| gr.Radio(choices=['FirstQ', 'SecondQ', 'ThirdQ', 'FourthQ'], value='SecondQ', label='Zip Income Quartile'), | |
| gr.Radio(choices=['Midwest', 'Northeast', 'South', 'West'], value='South', label='Hospital Region'), | |
| gr.Radio(choices=['New England', 'Middle Atlantic', 'East North Central', 'West North Central', 'South Atlantic', 'East South Central', 'West South Central', 'Mountain', 'Pacific'], value='South Atlantic', label='Hospital Division'), | |
| gr.Radio(choices=['Urban teaching', 'Urban nonteaching', 'Rural'], value='Urban teaching', label='Hospital Location/Teaching'), | |
| gr.Radio(choices=['Small', 'Medium', 'Large'], value='Large', label='Hospital Bedsize'), | |
| gr.Radio(choices=['Government_nonfederal', 'Private_invest_own', 'Private_not_profit'], value='Private_not_profit', label='Hospital Control'), | |
| gr.Dropdown(choices=['Private insurance', 'Medicare', 'Medicaid', 'Self-pay', 'No charge', 'Other'], value='Medicare', label='Payee'), | |
| # Comorbidities — default to "No" with some "Yes" for making the default selection more aesthetically pleasing | |
| gr.Radio(choices=["No", "Yes"], value="Yes", label='Anemia'), | |
| gr.Radio(choices=["No", "Yes"], value="Yes", label='Atrial Fibrillation'), | |
| gr.Radio(choices=["No", "Yes"], value="No", label='Cancer'), | |
| gr.Radio(choices=["No", "Yes"], value="Yes", label='Cardiac Arrhythmias'), | |
| gr.Radio(choices=["No", "Yes"], value="No", label='Carotid Artery Disease'), | |
| gr.Radio(choices=["No", "Yes"], value="Yes", label='Chronic Kidney Disease'), | |
| gr.Radio(choices=["No", "Yes"], value="Yes", label='Chronic Pulmonary Disease'), | |
| gr.Radio(choices=["No", "Yes"], value="No", label='Coagulopathy'), | |
| gr.Radio(choices=["No", "Yes"], value="No", label='Depression'), | |
| gr.Radio(choices=["No", "Yes"], value="Yes", label='Diabetes Mellitus'), | |
| gr.Radio(choices=["No", "Yes"], value="No", label='Drug Abuse'), | |
| gr.Radio(choices=["No", "Yes"], value="Yes", label='Dyslipidemia'), | |
| gr.Radio(choices=["No", "Yes"], value="No", label='Endocarditis'), | |
| gr.Radio(choices=["No", "Yes"], value="No", label='Family History'), | |
| gr.Radio(choices=["No", "Yes"], value="Yes", label='Fluid and Electrolyte Disorder'), | |
| gr.Radio(choices=["No", "Yes"], value="Yes", label='Heart Failure'), | |
| gr.Radio(choices=["No", "Yes"], value="Yes", label='Hypertension'), | |
| gr.Radio(choices=["No", "Yes"], value="Yes", label='Known CAD'), | |
| gr.Radio(choices=["No", "Yes"], value="No", label='Liver Disease'), | |
| gr.Radio(choices=["No", "Yes"], value="Yes", label='Obesity'), | |
| gr.Radio(choices=["No", "Yes"], value="Yes", label='Peripheral Vascular Disease'), | |
| gr.Radio(choices=["No", "Yes"], value="Yes", label='Prior CABG'), | |
| gr.Radio(choices=["No", "Yes"], value="Yes", label='Prior ICD'), | |
| gr.Radio(choices=["No", "Yes"], value="Yes", label='Prior MI'), | |
| gr.Radio(choices=["No", "Yes"], value="Yes", label='Prior PCI'), | |
| gr.Radio(choices=["No", "Yes"], value="Yes", label='Prior PPM'), | |
| gr.Radio(choices=["No", "Yes"], value="Yes", label='Prior TIA Stroke'), | |
| gr.Radio(choices=["No", "Yes"], value="No", label='Pulmonary Circulation Disorder'), | |
| gr.Radio(choices=["No", "Yes"], value="No", label='Smoker'), | |
| gr.Radio(choices=["No", "Yes"], value="Yes", label='Valvular Disease'), | |
| gr.Radio(choices=["No", "Yes"], value="No", label='Weight Loss'), | |
| gr.Radio(choices=["No", "Yes"], value="Yes", label='Endovascular TAVR'), | |
| gr.Radio(choices=["No", "Yes"], value="Yes", label='Transapical TAVR') | |
| ] | |
| # The app's first few descriptive lines | |
| description_html = """ | |
| <p style="font-size:16px; line-height:1.6;"> | |
| This app predicts in-hospital mortality after TAVR using a finalized logistic regression model with L2 penalty, based on national inpatient data from 2012–2019 (HCUP NIS).<br> | |
| <br> | |
| Published paper: | |
| <a href="https://www.nature.com/articles/s41598-023-37358-9.pdf" target="_blank"> | |
| Alhwiti, T., Aldrugh, S., & Megahed, F. M. (2023), <i>Scientific Reports</i> | |
| </a> | |
| </p> | |
| """ | |
| # Defining and launching the interface | |
| iface = gr.Interface( | |
| fn = predict, | |
| inputs = inputs, | |
| outputs = 'text', | |
| live=True, | |
| title = "Predicting In-Hospital Mortality After TAVR Using Preoperative Variables and Penalized Logistic Regression", | |
| description = description_html, | |
| css = 'https://bootswatch.com/5/journal/bootstrap.css') | |
| iface.launch() |