Enhancing-ICU-Mortality-Prediction-A-Machine-Learning-Approach-to-Identify-Key-Clinical-Risk-Factors

This project focuses on developing a machine learning model to predict mortality in Intensive Care Units (ICUs) based on clinical parameters recorded during patient stays. By analyzing large datasets with various clinical variables, the model aims to enhance predictive accuracy beyond traditional scoring systems.

Background:

In intensive care units (ICUs), timely and accurate identification of patients at high risk of mortality is crucial for guiding treatment decisions and optimizing care. While ICU clinicians rely on their experience and standard clinical scoring systems (like APACHE) to predict outcomes, there is potential for machine learning (ML) to enhance predictive accuracy by analyzing patterns in large datasets with multiple clinical variables.

Objective:

The goal of this project is to develop a machine learning model that predicts ICU mortality based on clinical parameters recorded during a patient’s stay. Additionally, the project aims to uncover the key clinical features most strongly associated with patient outcomes, which can provide valuable insights into ICU patient management.