An Interpretable Intensive Care Unit Mortality Risk Calculator

TL;DR

This paper develops an interpretable ICU mortality risk calculator using various machine learning models on the MIMIC-III database, emphasizing feature importance for clinical insights.

Contribution

It introduces an interpretable risk prediction framework combining multiple ML techniques and compares their feature importance, enhancing clinical interpretability.

Findings

High agreement on key features like cardiac surgery recovery, age, and blood urea nitrogen levels.

Models provide consistent insights into factors influencing mortality risk.

Potential to improve clinical decision-making through interpretable predictions.

Abstract

Mortality risk is a major concern to patients have just been discharged from the intensive care unit (ICU). Many studies have been directed to construct machine learning models to predict such risk. Although these models are highly accurate, they are less amenable to interpretation and clinicians are typically unable to gain further insights into the patients' health conditions and the underlying factors that influence their mortality risk. In this paper, we use patients' profiles extracted from the MIMIC-III clinical database to construct risk calculators based on different machine learning techniques such as logistic regression, decision trees, random forests and multilayer perceptrons. We perform an extensive benchmarking study that compares the most salient features as predicted by various methods. We observe a high degree of agreement across the considered machine learning methods; in particular, the cardiac surgery recovery unit, age, and blood urea nitrogen levels are commonly predicted to be the most salient features for determining patients' mortality risks. Our work has the potential for clinicians to interpret risk predictions.