Case Identification of Depression in Inpatient Electronic Medical Records: Scoping Review.

IF 3.1 3区医学 Q2 MEDICAL INFORMATICS JMIR Medical Informatics Pub Date : 2024-10-14 DOI:10.2196/49781

Allison Grothman, William J Ma, Kendra G Tickner, Elliot A Martin, Danielle A Southern, Hude Quan

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Abstract

Background: Electronic medical records (EMRs) contain large amounts of detailed clinical information. Using medical record review to identify conditions within large quantities of EMRs can be time-consuming and inefficient. EMR-based phenotyping using machine learning and natural language processing algorithms is a continually developing area of study that holds potential for numerous mental health disorders.

Objective: This review evaluates the current state of EMR-based case identification for depression and provides guidance on using current algorithms and constructing new ones.

Methods: A scoping review of EMR-based algorithms for phenotyping depression was completed. This research encompassed studies published from January 2000 to May 2023. The search involved 3 databases: Embase, MEDLINE, and APA PsycInfo. This was carried out using selected keywords that fell into 3 categories: terms connected with EMRs, terms connected to case identification, and terms pertaining to depression. This study adhered to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines.

Results: A total of 20 papers were assessed and summarized in the review. Most of these studies were undertaken in the United States, accounting for 75% (15/20). The United Kingdom and Spain followed this, accounting for 15% (3/20) and 10% (2/20) of the studies, respectively. Both data-driven and clinical rule-based methodologies were identified. The development of EMR-based phenotypes and algorithms indicates the data accessibility permitted by each health system, which led to varying performance levels among different algorithms.

Conclusions: Better use of structured and unstructured EMR components through techniques such as machine learning and natural language processing has the potential to improve depression phenotyping. However, more validation must be carried out to have confidence in depression case identification algorithms in general.

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住院病人电子病历中的抑郁症病例识别：范围审查。

背景：电子病历（EMR）包含大量详细的临床信息：电子病历（EMR）包含大量详细的临床信息。使用病历审查来识别大量 EMR 中的病症既耗时又低效。使用机器学习和自然语言处理算法进行基于 EMR 的表型分析是一个不断发展的研究领域，对许多心理健康疾病都有潜在的帮助：本综述评估了基于 EMR 的抑郁症病例识别的现状，并为使用现有算法和构建新算法提供指导：方法：我们完成了对基于电子病历的抑郁症表型算法的范围综述。这项研究涵盖了 2000 年 1 月至 2023 年 5 月期间发表的研究。检索涉及 3 个数据库：Embase、MEDLINE 和 APA PsycInfo。检索时使用的关键词分为三类：与 EMR 相关的术语、与病例识别相关的术语以及与抑郁症相关的术语。这项研究遵循了 PRISMA-ScR（系统性综述和元分析的首选报告项目，范围综述的扩展）指南：本综述共评估和总结了 20 篇论文。这些研究大多在美国进行，占 75%（15/20）。英国和西班牙紧随其后，分别占 15%（3/20）和 10%（2/20）。研究发现了数据驱动和基于临床规则的方法。基于EMR的表型和算法的发展表明了每个医疗系统允许的数据可访问性，这导致不同算法的性能水平各不相同：结论：通过机器学习和自然语言处理等技术更好地利用结构化和非结构化的 EMR 组件有可能改善抑郁症的表型。然而，要对抑郁症病例识别算法有信心，还必须进行更多的验证。

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

JMIR Medical Informatics Medicine-Health Informatics

CiteScore

7.90

自引率

3.10%

发文量

173

审稿时长

12 weeks

期刊介绍： JMIR Medical Informatics (JMI, ISSN 2291-9694) is a top-rated, tier A journal which focuses on clinical informatics, big data in health and health care, decision support for health professionals, electronic health records, ehealth infrastructures and implementation. It has a focus on applied, translational research, with a broad readership including clinicians, CIOs, engineers, industry and health informatics professionals. Published by JMIR Publications, publisher of the Journal of Medical Internet Research (JMIR), the leading eHealth/mHealth journal (Impact Factor 2016: 5.175), JMIR Med Inform has a slightly different scope (emphasizing more on applications for clinicians and health professionals rather than consumers/citizens, which is the focus of JMIR), publishes even faster, and also allows papers which are more technical or more formative than what would be published in the Journal of Medical Internet Research.