Predictive Modeling of ICU Outcomes in Sepsis Patients
A clinical ML prototype for predicting ICU mortality and length of stay for sepsis patients using MIMIC-IV.
Built and evaluated ensemble machine learning models (Random Forest, XGBoost, LightGBM, AdaBoost) to predict sepsis patient ICU outcomes (in-hospital mortality and ICU length of stay), with careful handling of class imbalance and feature selection, and developed an interactive visualization tool to help clinicians interpret model predictions.
Part of the Capstone Project under Prof. Lipika Dey, in collaboration with Maanas Kejriwal, Fall 2024.
Project titled: “Data-Driven Exploration: Unlocking Predictive Potential and Insights into ICU Outcomes in Sepsis”