机器学习模型对危急或恶化的健康状况的响应能力较低
Tanmoy Sarkar Pias1, Sharmin Afrose2, Moon Das Tuli3
1Department of Computer Science and Sanghani Center for AI and Data Analytics, Virginia Tech, Blacksburg, VA, USA.
对于患者护理的机器学习模型具有危险的盲点,无法检测关键条件. 纳入医学知识对于提高临床机器学习安全性和可靠性至关重要.
科学领域:
- 医疗人工智能 医疗人工智能
- 临床决策支持系统 临床决策支持系统
背景情况:
- 机器学习 (ML) 模型对于预测重症监护病房患者死亡率非常有价值.
- 有效的ML模型应该提醒医生注意患者的快速恶化或异常生命体征.
- 在临床部署之前,对ML模型性能进行彻底评估至关重要.
研究的目的:
- 开发和评估医疗ML模型的新型测试方法.
- 系统地评估ML模型对关键患者状况的响应能力.
- 发现ML模型识别严重医疗情况的能力中的缺陷.
主要方法:
- 开发了梯度上升和神经激活地图测试方法.
- 利用合成的测试案例,包括时间序列数据,来评估ML模型.
- 医生参与指导在多个ML模型,数据集和预测任务中的评估.
主要成果:
- 鉴定了ML模型对关键条件的响应能力的重大缺陷.
- 机器学习模型未能在医院死亡率预测任务中识别66%的受伤.
- 在癌症预测模型中观察到类似的响应性问题,突出显示了系统性ML模型的局限性.
结论:
- 仅在患者数据上训练的统计ML模型表现出危险的盲点.
- 大多数经过测试的ML模型对重症患者的反应不足.
- 将医学知识整合到临床ML模型中是未来研究的关键领域.
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