利用可解释的人工智能来优化临床决策支持
Siru Liu1,2, Allison B McCoy1, Josh F Peterson1,3
1Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States.
Journal of the American Medical Informatics Association : JAMIA
|February 21, 2024
概括
本研究引入了一种基于数据的方法,使用可解释的人工智能 (XAI) 来增强临床决策支持 (CDS) 预警标准. 该方法成功地确定了改善警报和发现工作流的问题的有用建议.
科学领域:
- 临床信息学 临床信息学
- 医疗保健中的人工智能
- 机器学习用于医疗决策支持
背景情况:
- 临床决策支持 (CDS) 警报对于患者护理至关重要,但往往需要优化.
- 手动审查警报标准可能耗时,可能会错过潜在的改进.
- 可解释的人工智能 (XAI) 为理解和完善复杂系统提供了新的方法.
研究的目的:
- 开发和评估一个数据驱动的过程,以产生可行的建议,以改善CDS警报标准.
- 利用可解释的人工智能 (XAI) 技术来识别最佳警报参数.
- 评估XAI产生的建议与历史数据和专家临床判断的有用性.
主要方法:
- 从范德比尔特大学医学中心 (2019-2020) 提取了警报数据.
- 开发机器学习模型来预测用户对警报的反应.
- 应用了XAI技术来解释警报行为的全球和本地解释.
- 通过与警报变更日志进行比较并对利益相关者进行采访来评估建议.
主要成果:
- 使用了2,991,823个警报发射的数据集,其中有2689个特征.
- 轻GBM模型实现了0.919的ROC曲线下的高面积.
- 确定了96个有用的建议,以改善警报标准.
- 确定9.3%的警报发射可能会被消除.
- 未发现的工作流程和与教育相关的问题影响了警报的接受.
结论:
- 使用XAI的数据驱动过程有效地产生了改善CDS警报标准的建议.
- 这种方法可以识别手动审查可能忽略或延迟的改进.
- XAI具有双重目的:优化警报标准和发现与工作流程,教育或人员配置相关的质量问题.
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