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.
Abstract (translated)
人工智能支持医疗专业人员运用预测建模，极大地改善了临床决策。这项研究关注公正性和可解释性在医疗保健应用程序中的重要性，以确保不同患者群体之间实现公正的结局。我们聚焦于脓毒症相关死亡预测，提出了一种学习性能优化预测模型的方法，然后应用迁移学习过程产生更公平的模型。我们还在方法中引入了一种新型的置换基特征重要性算法，旨在阐明每个特征在增强预测公正性方面的贡献。与现有的解释性方法侧重于解释特征对预测性能的贡献不同，我们提出的方法独特地弥合了理解每个特征如何影响公正性的差距。鉴于脓毒症的高死亡率及其在医院死亡中的重要作用，我们方法的进步至关重要。通过改进模型的透明度和公平性，它不仅有助于识别和减轻预测模型中的偏见，而且还有助于培养医疗保健利益相关者之间的信任，从而为更公正、可信赖的医疗保健提供贡献。
URL
https://arxiv.org/abs/2404.13139
