Explainable AI for Fair Sepsis Mortality Predictive Model
This work addresses fairness and explainability in healthcare AI for sepsis mortality prediction, which is crucial for equitable patient care, though it appears incremental as it builds on existing transfer learning and explainability techniques.
The study tackled the need for fairness and explainability in AI for sepsis mortality prediction by proposing a method that uses transfer learning to improve fairness and a novel permutation-based feature importance algorithm to explain feature contributions to fairness, resulting in a model with enhanced transparency and equitable outcomes.
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.