Assessment of Prediction Tasks and Time Window Selection in Temporal Modeling of Electronic Health Record Data: a Systematic Review.

IF 5.9 Q1 Computer Science
Journal of Healthcare Informatics Research Pub Date : 2023-08-14 eCollection Date: 2023-09-01 DOI:10.1007/s41666-023-00143-4
Sarah Pungitore, Vignesh Subbian
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引用次数: 0

Abstract

Temporal electronic health record (EHR) data are often preferred for clinical prediction tasks because they offer more complete representations of a patient's pathophysiology than static data. A challenge when working with temporal EHR data is problem formulation, which includes defining the time windows of interest and the prediction task. Our objective was to conduct a systematic review that assessed the definition and reporting of concepts relevant to temporal clinical prediction tasks. We searched PubMed® and IEEE Xplore® databases for studies from January 1, 2010 applying machine learning models to EHR data for patient outcome prediction. Publications applying time-series methods were selected for further review. We identified 92 studies and summarized them by clinical context and definition and reporting of the prediction problem. For the time windows of interest, 12 studies did not discuss window lengths, 57 used a single set of window lengths, and 23 evaluated the relationship between window length and model performance. We also found that 72 studies had appropriate reporting of the prediction task. However, evaluation of prediction problem formulation for temporal EHR data was complicated by heterogeneity in assessing and reporting of these concepts. Even among studies modeling similar clinical outcomes, there were variations in terminology used to describe the prediction problem, rationale for window lengths, and determination of the outcome of interest. As temporal modeling using EHR data expands, minimal reporting standards should include time-series specific concerns to promote rigor and reproducibility in future studies and facilitate model implementation in clinical settings.

Supplementary information: The online version contains supplementary material available at 10.1007/s41666-023-00143-4.

电子健康记录数据时间建模中预测任务和时间窗口选择的评估:系统综述。
时间电子健康记录(EHR)数据通常是临床预测任务的首选数据,因为它们比静态数据更完整地表示患者的病理生理学。处理时间EHR数据时的一个挑战是问题公式化,其中包括定义感兴趣的时间窗口和预测任务。我们的目的是进行一项系统综述,评估与时间临床预测任务相关的概念的定义和报告。我们在PubMed®和IEEE Xplore®数据库中搜索了2010年1月1日以来的研究,将机器学习模型应用于EHR数据,用于患者结果预测。选择采用时间序列方法的出版物进行进一步审查。我们确定了92项研究,并通过临床背景、预测问题的定义和报告对其进行了总结。对于感兴趣的时间窗口,12项研究没有讨论窗口长度,57项使用了一组窗口长度,23项评估了窗口长度与模型性能之间的关系。我们还发现,72项研究对预测任务有适当的报告。然而,由于评估和报告这些概念的异质性,对时间EHR数据的预测问题公式的评估变得复杂。即使在模拟类似临床结果的研究中,用于描述预测问题的术语、窗口长度的基本原理以及感兴趣结果的确定也存在差异。随着使用EHR数据的时间建模的扩展,最低报告标准应包括特定于时间序列的问题,以提高未来研究的严谨性和再现性,并促进模型在临床环境中的实施。补充信息:在线版本包含补充材料,可访问10.1007/s41666-023-00143-4。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Healthcare Informatics Research
Journal of Healthcare Informatics Research Computer Science-Computer Science Applications
CiteScore
13.60
自引率
1.70%
发文量
12
期刊介绍: Journal of Healthcare Informatics Research serves as a publication venue for the innovative technical contributions highlighting analytics, systems, and human factors research in healthcare informatics.Journal of Healthcare Informatics Research is concerned with the application of computer science principles, information science principles, information technology, and communication technology to address problems in healthcare, and everyday wellness. Journal of Healthcare Informatics Research highlights the most cutting-edge technical contributions in computing-oriented healthcare informatics.  The journal covers three major tracks: (1) analytics—focuses on data analytics, knowledge discovery, predictive modeling; (2) systems—focuses on building healthcare informatics systems (e.g., architecture, framework, design, engineering, and application); (3) human factors—focuses on understanding users or context, interface design, health behavior, and user studies of healthcare informatics applications.   Topics include but are not limited to: ·         healthcare software architecture, framework, design, and engineering;·         electronic health records·         medical data mining·         predictive modeling·         medical information retrieval·         medical natural language processing·         healthcare information systems·         smart health and connected health·         social media analytics·         mobile healthcare·         medical signal processing·         human factors in healthcare·         usability studies in healthcare·         user-interface design for medical devices and healthcare software·         health service delivery·         health games·         security and privacy in healthcare·         medical recommender system·         healthcare workflow management·         disease profiling and personalized treatment·         visualization of medical data·         intelligent medical devices and sensors·         RFID solutions for healthcare·         healthcare decision analytics and support systems·         epidemiological surveillance systems and intervention modeling·         consumer and clinician health information needs, seeking, sharing, and use·         semantic Web, linked data, and ontology·         collaboration technologies for healthcare·         assistive and adaptive ubiquitous computing technologies·         statistics and quality of medical data·         healthcare delivery in developing countries·         health systems modeling and simulation·         computer-aided diagnosis
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