一个移动应用程序解决了基于机器学习的糖尿病预测中的解释性挑战:基于调查的用户研究
Rasha Hendawi1, Juan Li1, Souradip Roy1
1North Dakota State University, Fargo, ND, United States.
JMIR formative research
|November 13, 2023
概括
糖尿病护理的可解释AI (XAI) 提高了理解和信任. XAI4Diabetes平台增强了医疗保健专业人员对AI预测及其潜在推理的理解.
科学领域:
- 医疗保健中的人工智能
- 机器学习用于疾病预测和预测
- 可解释的人工智能 (XAI)
背景情况:
- 机器学习 (ML) 模型对于糖尿病诊断和预测是有效的,但通常作为"黑盒子".
- 由于缺乏信任和理解,ML模型的不透明性阻碍了医疗保健中的采用.
- 这种缺乏透明度是将AI整合到糖尿病护理中的重要障碍.
研究的目的:
- 开发和评估XAI4Diabetes,这是一个可解释的糖尿病护理人工智能平台.
- 为医疗保健专业人员提供可解释的AI预测和建议.
- 提高对复杂的ML模型及其在糖尿病风险评估中的结果的理解.
主要方法:
- 开发了一个多模块解释框架 (XAI4Diabetes),使用ML,知识图和本体学.
- 该平台包括知识库,知识匹配,预测和解释的模块.
- 通过对医疗专业人员进行基于调查的用户研究,评估可用性和对理解的影响.
主要成果:
- 一个原型移动应用程序被开发并通过可用性研究和满意度调查进行评估.
- 医疗专业人员对糖尿病预测过程,数据集,特征和特征意义有了更好的理解.
- 参与者在使用XAI4Diabetes后报告了对AI预测的信任和理解增加,总体满意度很高.
结论:
- XAI4Diabetes是一个多功能平台,可以解释糖尿病风险预测,提高透明度.
- 该平台使医疗保健专业人员能够理解人工智能决策,培养信任和减轻偏见.
- XAI4Diabetes通过解决可解释性的需求,促进了更广泛的AI整合到糖尿病护理中.
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