Explainable AI for Fair Sepsis Mortality Predictive Model

arXiv:2404.13139v1 Announce Type: new
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 …

abstract ai applications applications artificial artificial intelligence arxiv clinical cs.ai cs.lg decision demographics diverse explainability explainable ai fair fairness healthcare intelligence making modeling mortality patient predictive predictive modeling professionals sepsis study type