Explainable AI for Fair Sepsis Mortality Predictive Model

TL;DR

This paper introduces a novel approach to enhance fairness and explainability in AI models predicting sepsis mortality, aiming to reduce biases and improve trust in healthcare decision-making.

Contribution

It presents a transfer learning-based method combined with a permutation feature importance algorithm to improve fairness and elucidate feature contributions in predictive models.

Findings

Enhanced fairness in sepsis mortality prediction models

Improved transparency through permutation-based feature importance

Potential reduction of biases in healthcare AI applications

Abstract

Artificial intelligence supports healthcare professionals with predictive modeling, greatly transforming clinical decision-making. This study addresses the crucial need for fairness and explainability in AI applications within healthcare to ensure equitable outcomes across diverse patient demographics. By focusing on the predictive modeling of sepsis-related mortality, we propose a method that learns a performance-optimized predictive model and then employs the transfer learning process to produce a model with better fairness. Our method also introduces a novel permutation-based feature importance algorithm aiming at elucidating the contribution of each feature in enhancing fairness on predictions. Unlike existing explainability methods concentrating on explaining feature contribution to predictive performance, our proposed method uniquely bridges the gap in understanding how each feature contributes to fairness. This advancement is pivotal, given sepsis's significant mortality rate and its role in one-third of hospital deaths. Our method not only aids in identifying and mitigating biases within the predictive model but also fosters trust among healthcare stakeholders by improving the transparency and fairness of model predictions, thereby contributing to more equitable and trustworthy healthcare delivery.