在临床预测模型中将来自EHR的信息收集的实验室数据纳入临床预测模型
Minghui Sun1, Matthew M Engelhard2, Armando D Bedoya3
1Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA. ms1008@duke.edu.
BMC medical informatics and decision making
|July 25, 2024
概括
在电子健康记录 (EHR) 中处理信息性缺失数据对于准确的临床预测模型 (CPM) 至关重要. 考虑非随机缺失 (NMAR) 数据的策略可以提高CPM性能,特别是嵌入方法.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 临床数据科学 临床数据科学
背景情况:
- 电子健康记录 (EHR) 是临床预测模型 (CPM) 的基础.
- 有关信息的缺失数据,特别是非随机缺失 (NMAR) 实验室值,是一个重大挑战.
- 标准的归算方法对于NMAR数据是不够的,需要专门的处理策略.
研究的目的:
- 为了比较临床预测模型的各种缺失数据处理策略的性能.
- 评估不同归算技术对预测快速住院病情恶化的模型的影响.
- 确定在EHR衍生的CPM中管理NMAR数据的最佳方法.
主要方法:
- 通过使用12项实验室测量和高失踪率 (50-90%),开发了一个快速住院病人的预测模型.
- 进行比较的归算策略包括平均归算,正常值归算,条件归算,分类编码和缺失嵌入,其中一些具有最后观察转载 (LOCF).
- 下游分类器包括后勤LASSO回归,多层感知器 (MLP) 和长短期记忆 (LSTM) 模型,其性能由AUROC和引导评估.
主要成果:
- 长短期记忆 (LSTM) 模型的表现普遍优于其他模型.
- 嵌入方法和分类编码在经过测试的策略中表现出优越的性能.
- 对于横截面模型,与LOCF相结合的正常值归算产生了最好的结果.
结论:
- 明确处理非随机缺失 (NMAR) 数据的策略显著提高了临床预测模型的性能.
- 嵌入方法的优点在于不需要先前的临床专业知识.
- 虽然Last Observation Carried Forward (LOCF) 可以使横截面模型受益,但其应用可能会对LSTM模型性能产生负面影响.
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