介绍了创伤护理中临床决策支持的概率模型的预测和性能
Cansu Alptekin1, Jared M Wohlgemut2, Zane B Perkins3
1Department of Cognitive Science, Graduate School of Informatics, Middle East Technical University, Turkey.
International journal of medical informatics
|November 23, 2024
概括
临床模型预测的可视化影响临床医生的判断. 不同的风险和绩效图表类型会影响解释,对于绩效数据更喜欢图标数组.
科学领域:
- 临床决策支持系统临床决策支持系统
- 医疗信息学医学信息学
- 健康数据可视化,健康数据可视化
背景情况:
- 临床预测模型提供了有价值的见解,但其呈现方式可能有所不同.
- 了解不同的视觉表示如何影响临床医生的解释对于有效实施至关重要.
研究的目的:
- 评估概率预测和模型性能的各种视觉呈现如何影响临床医生的风险判断和偏好.
- 评估不同图表类型对临床预测模型输出的解释的影响.
主要方法:
- 使用贝叶斯网络 (BN) 模型预测创伤诱导凝血病 (TIC) 风险.
- 临床医生提供了患者场景和TIC BN预测,使用各种条形图表,图标阵列和风险测量图表,以及ROC,速率曲线或图标阵列用于性能.
- 一项在线调查收集了关于风险判断,感知精度和图表偏好的数据.
主要成果:
- 对风险呈现的图表选择有所不同,特别是在边界中等风险场景中.
- 风险/绩效图表选择和感知精度之间的相互作用在高风险和低风险场景中不同.
- 临床医生对某些图表类型表现出一致的偏好,而用于性能可视化则倾向于图标数组.
结论:
- 呈现预测模型输出的方法显著影响风险和绩效的解释.
- 临床预测模型应该包含数据可视化的灵活选项,以满足用户的需求并增强理解.
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