Machine Learning For The Prediction Of In-hospital Mortality In Patients With Spontaneous Intracerebral Hemorrhage In Intensive Care Unit

This article discusses the use of Shapley Additive exPlanations (SHAP), a game-theoretic technique, to enhance the interpretability of machine learning models in healthcare. The study aims to develop a predictive model for in-hospital mortality among patients with non-traumatic subarachnoid hemorrhage using data from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database.