“NPC-based risk stratification model for necrotizing fasciitis using bootstrap and permutation methods.”

Real-time clinical risk stratification system using multi-factor analysis, vital sign monitoring, and machine learning for intelligent healthcare decision support.

Machine Learning for Patient Risk Stratification for Acute Respiratory Distress Syndrome (Zeiberg & Prahlad et al.), PLOS ONE, March 2019. https://doi.org/10.1371/journal.pone.0214465

Colorectal cancer risk mapping through Bayesian Networks

Presentation on risk stratification model

AI-powered Early Childhood Development Intelligence Platform for risk stratification, predictive insights, and personalized intervention planning. Built for scalable public health governance with role-based decision support, longitudinal tracking, and secure, explainable AI architecture.

Real-world hemodialysis data analysis focused on intradialytic hypotension, clinical risk patterns, and patient-level hemodynamic phenotyping.

A worked example to support the e-Learning for Health (e-LfH) PHM Risk Stratification Module.

Portfolio project: Clinical risk stratification & population health analytics using Python and Pandas.

METABRIC breast cancer survival analysis & ML workflow. Features KM curves, Cox models, Decision Trees, RFs, risk calibration, and Decision Curve Analysis.

Predição do risco de óbito de pacientes com COVID-19

HER2-Amplified vs Non-Amplified Breast Cancer Transcriptome Analysis (TCGA BRCA): Differential Expression, Pathway Enrichment, and Lasso-Cox Survival Stratification

Discovering healthcare insights through data-driven analysis of EHRs. This project comprises three subprojects: data cleaning & preparation, exploratory data analysis and lastly, predictive modeling for in-hospital mortality and clinical risk stratification.

Healthcare analytics system identifying hospitals at risk for CMS readmission penalties. Analyzes $5.96B in financial exposure, predicts intervention opportunities, saves $1.46B. Python • scikit-learn • CMS data • Healthcare policy

Hypertension Risk Stratification Using Machine Learning: Lift-Based Evaluation on SAS® Viya With the Framingham Heart Study

Examining Transcriptomic Markers Associated With Neutrophil Extracellular Traps to Predict Mortality Risk in Neonatal Sepsis

Wraps a standardized approach to risk stratification used by the intelligence function

RENALGUARD AI - CKD Management Platform for Primary Care Physicians

Capacity-aware hospital readmission prioritization with calibrated ML, workflow simulation, fairness analysis, and a Streamlit demo.

This repository features a high-integrity machine learning pipeline developed to assist clinical researchers in stratifying patient risk for lung cancer. By utilizing an optimized Logistic Regression framework and UMAP, the project emphasizes model interpretability—a critical requirement for clinical validation and regulatory transparency.