评估临床决策支持算法的准确性和公平性,当医疗保健资源有限时
Esther L Meerwijk1, Duncan C McElfresh2, Susana Martins2
1Program Evaluation and Resource Center, Office of Mental Health and Suicide Prevention, Department of Veterans Affairs, Menlo Park, CA, USA; VA Health Systems Research, Center for Innovation to Implementation (Ci2i), VA Palo Alto Health Care System, Menlo Park, CA, USA.
Journal of biomedical informatics
|June 8, 2024
概括
一个新的框架有助于评估患者干预的临床预测模型. "每真正阳性条"可视化有助于评估准确性和公平性,特别是在有限的医疗保健资源的情况下.
科学领域:
- 临床信息学是一种临床信息学.
- 医疗服务研究 医疗服务研究
- 机器学习在医疗保健中的应用
背景情况:
- 预测患者风险的临床模型往往缺乏跨子组公平性的明确评估指标,特别是当干预资源有限时.
- 现有的绩效指标可能无法充分捕捉高风险临床决策中的准确性和公平性问题.
- 评估算法公平性对于公平的医疗保健资源分配至关重要.
研究的目的:
- 提出一个衡量标准框架,用于评估标记患者进行干预的临床模型的准确性和算法公平性.
- 解决在资源有限的医疗保健机构评估模型绩效的指导方面的差距.
- 引入一种新的可视化工具,以了解模型预测及其公平性影响.
主要方法:
- 应用了一套指标,包括错误的阳性/阴性/遗漏率和新的"每真阳性条"可视化,用于大型退伍军人卫生管理局 (VA) 临床模型.
- 对高危过量或自杀事件的患者,评估了该模型在各年龄小组的性能和公平性.
- 将标准指标 (如ROC AUC,精度回忆) 的实用性与针对特定用例的建议指标进行比较.
主要成果:
- 错误的阳性率,错误的阴性率,错误的遗漏率和"每真正的阳性条"在揭示群体差异和评估公平性方面最有效.
- 像ROC AUC,校准曲线和精度回忆曲线这样的标准指标在这个特定的应用中具有有限的实用性.
- "每一个真正阳性条"为平衡虚假阳性和虚假阴性提供了直观的可视化.
结论:
- 没有一个单一的指标足以在临床环境中进行全面的模型性能监测和偏差分析.
- 拟议的框架和"每真正正条"为研究人员和临床医生在资源有限的环境中评估预测模型提供了有价值的工具.
- "每真正正条"可视化增强了利益相关者的解释性,有助于为关键事件预测选择值.
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