用公平的机器学习模型预测术后慢性阿片类药物使用,整合多模式数据源:在医疗保健中展示伦理机器学习
Nidhi Soley1,2, Ilia Rattsev1,2, Traci J Speed3
1Institute for Computational Medicine, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD 21218, United States.
概括
将基因组数据集成到人工智能模型中可以提高慢性阿片类药物使用预测的准确性. 偏差缓解技术确保在不同人群中进行公平的风险评估,增强医疗保健AI.
科学领域:
- 医疗保健中的人工智能
- 基因组数据分析 基因组数据分析
- 预测建模预测建模
背景情况:
- 以前的研究建立了电子健康记录 (EHR) 和可穿戴数据,用于预测慢性阿片类药物使用.
- 整个人工智能生命周期 (HEAAL) 的健康公平框架指导着公平的人工智能的发展.
研究的目的:
- 将现有的预测模型与基因组数据进行微调,以提高慢性阿片类药物使用预测.
- 使用IBM的AIF360应用偏见缓解技术来解决性别和种族差异.
- 通过使用各种指标来评估模型性能和公平性.
主要方法:
- 利用了来自271名"我们所有人"研究计划参与者的EHR,可穿戴设备和基因组数据.
- 微调了四个机器学习模型,并使用了SHapley添加式扩展 (SHAP) 来进行预测器识别.
- 应用了IBM的AIF360预处理工具包,以减轻性别和种族的偏见.
主要成果:
- 基因组数据集成改善了模型性能,使曲线下的面积从0.90增加到0.95.
- 关键预测因素包括多巴胺D1受体 (DRD1) rs4532,手术类型和体育活动持续时间.
- 偏见缓解技术增强了跨种族和性别群体的模式公平性,而不会降低绩效.
结论:
- 该研究成功地利用了HEAAL框架来开发一个公平的AI解决方案,用于预测慢性阿片类药物使用.
- 包括基因组学在内的多模式数据和偏差缓解对于在不同人群中进行准确和公平的风险评估至关重要.
- 这项工作促进了医疗保健中人工智能应用的公平性.
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