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An ExplainableFair Framework for Prediction of Substance Use Disorder Treatment Completion
Conference paper   Open access

An ExplainableFair Framework for Prediction of Substance Use Disorder Treatment Completion

Mary M. Lucas, Xiaoyang Wang, Chia-Hsuan Chang, Christopher C. Yang, Jacqueline E. Braughton and Quyen M. Ngo
2024 IEEE 12th International Conference on Healthcare Informatics (ICHI), pp 157-166
03 Jun 2024
DOI: https://doi.org/10.1109/ICHI61247.2024.00028
url
https://doi.org/10.48550/arXiv.2404.03833View
Submitted Open CC BY V4.0

Abstract

bias Decision making explainability fairness in-processing Medical services predictive model Predictive models Sensitivity substance use disorder Training Visualization Machine Learning
Fairness of machine learning models in healthcare has drawn increasing attention from clinicians, researchers, and even at the highest level of government. On the other hand, the importance of developing and deploying interpretable or explainable models has been demonstrated, and is essential to increasing the trustworthiness and likelihood of adoption of these models. The objective of this study was to develop and implement a framework for addressing both these issues - fairness and explainability. We propose an explainable fairness framework, first developing a model with optimized performance, and then using an in-processing approach to mitigate model biases relative to the sensitive attributes of race and sex. We then explore and visualize explanations of the model changes that lead to the fairness enhancement process through exploring the changes in importance of features. Our resulting-fairness enhanced models retain high sensitivity with improved fairness and explanations of the fairness-enhancement that may provide helpful insights for healthcare providers to guide clinical decision-making and resource allocation.

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Web of Science research areas
Computer Science, Artificial Intelligence
Computer Science, Information Systems
Medical Informatics
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