Conference proceeding
Explainable AI for Fair Sepsis Mortality Predictive Model
Artificial Intelligence in Medicine, PT II, AIME 2024, v 14845, pp 267-276
01 Jan 2024
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.
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Details
- Title
- Explainable AI for Fair Sepsis Mortality Predictive Model
- Creators
- Chia-Hsuan Chang - Drexel UniversityXiaoyang Wang - Drexel UniversityChristopher C. Yang (Corresponding Author) - Drexel University
- Contributors
- Joseph Finkelstein (Editor) - University of UtahRobert Moskovitch (Editor) - Ben-Gurion University of the NegevEnea Parimbelli (Editor) - University of Pavia
- Publication Details
- Artificial Intelligence in Medicine, PT II, AIME 2024, v 14845, pp 267-276
- Conference
- 22nd International Conference, AIME 2024, 22nd (Salt Lake City, Utah, United States, 09 Jul 2024–12 Jul 2024)
- Series
- Lecture Notes in Artificial Intelligence; 14845
- Publisher
- Springer Nature
- Number of pages
- 10
- Grant note
- National Science Foundation; National Science Foundation (NSF) DoD W91XWH-05-1-023 / Department of Defense; United States Department of Defense
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science; School of Computer and Information Sciences
- Web of Science ID
- WOS:001295133500029
- Scopus ID
- 2-s2.0-85206216705
- Other Identifier
- 991022198378204721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Engineering, Biomedical
- Radiology, Nuclear Medicine & Medical Imaging