电子医疗记录中的病理生理特征在时间数据集转移下维持模型性能
Raphael Brosula1,2, Conor K Corbin3, Jonathan H Chen4
1Genomic Center for Infectious Diseases, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
概括
了解电子病历 (EMR) 功能是强大的机器学习 (ML) 模型的关键. 病理生理特征可以随着时间的推移改善模型性能,减轻时间数据集的转移.
科学领域:
- 临床信息学是一种临床信息学.
- 机器学习在医疗保健中的应用
- 健康数据科学健康数据科学
背景情况:
- 电子病历 (EMR) 对于在临床环境中开发监督机器学习 (ML) 模型至关重要.
- 关于特定的EMR特征如何影响ML模型在时间数据集转移期间的表现的研究有限.
研究的目的:
- 分析EMR特征组和类别对ML模型初始和持续性能的影响.
- 了解不同特征类型如何影响对时间数据集转移的模型稳定性.
主要方法:
- 来自EMR的特征被汇总成基于来源的特征组 (例如药物,诊断,实验室).
- 根据患者病理生理学或医疗保健过程,特征被分类.
- 用调整的沙普利值来量化特征组和类别的边际贡献.
主要成果:
- 特性对初始模型性能的贡献在不同的临床预测任务中有所不同.
- 反映患者病理生理学的特征被发现对于减少随时间推移的性能下降至关重要.
- 特定特征组对初始性能和对时间转移的稳定性都产生了不同的影响.
结论:
- 对特征贡献的可解释性见解可以指导开发更强大的临床ML模型.
- 优先考虑病理生理特征可能会提高ML模型面临时间数据集转移的长期可靠性.
- 本研究为评估在动态医疗数据环境中的特征影响提供了一个框架.
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