Department of Oral Public Health, Academic Centre for Dentistry Amsterdam (ACTA), University of Amsterdam and Vrije Universiteit, Amsterdam, Netherlands.
Department Oral Hygiene, Cluster Health, Sports and Welfare, Inholland University of Applied Sciences, Amsterdam, Netherlands.
J Med Internet Res. 2024 Aug 20;26:e48320. doi: 10.2196/48320.
Electronic health records (EHRs) contain patients' health information over time, including possible early indicators of disease. However, the increasing amount of data hinders clinicians from using them. There is accumulating evidence suggesting that machine learning (ML) and deep learning (DL) can assist clinicians in analyzing these large-scale EHRs, as algorithms thrive on high volumes of data. Although ML has become well developed, studies mainly focus on engineering but lack medical outcomes.
This study aims for a scoping review of the evidence on how the use of ML on longitudinal EHRs can support the early detection and prevention of disease. The medical insights and clinical benefits that have been generated were investigated by reviewing applications in a variety of diseases.
This study was conducted according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. A literature search was performed in 2022 in collaboration with a medical information specialist in the following databases: PubMed, Embase, Web of Science Core Collection (Clarivate Analytics), and IEEE Xplore Digital Library and computer science bibliography. Studies were eligible when longitudinal EHRs were used that aimed for the early detection of disease via ML in a prevention context. Studies with a technical focus or using imaging or hospital admission data were beyond the scope of this review. Study screening and selection and data extraction were performed independently by 2 researchers.
In total, 20 studies were included, mainly published between 2018 and 2022. They showed that a variety of diseases could be detected or predicted, particularly diabetes; kidney diseases; diseases of the circulatory system; and mental, behavioral, and neurodevelopmental disorders. Demographics, symptoms, procedures, laboratory test results, diagnoses, medications, and BMI were frequently used EHR data in basic recurrent neural network or long short-term memory techniques. By developing and comparing ML and DL models, medical insights such as a high diagnostic performance, an earlier detection, the most important predictors, and additional health indicators were obtained. A clinical benefit that has been evaluated positively was preliminary screening. If these models are applied in practice, patients might also benefit from personalized health care and prevention, with practical benefits such as workload reduction and policy insights.
Longitudinal EHRs proved to be helpful for support in health care. Current ML models on EHRs can support the detection of diseases in terms of accuracy and offer preliminary screening benefits. Regarding the prevention of diseases, ML and specifically DL models can accurately predict or detect diseases earlier than current clinical diagnoses. Adding personally responsible factors allows targeted prevention interventions. While ML models based on textual EHRs are still in the developmental stage, they have high potential to support clinicians and the health care system and improve patient outcomes.
电子健康记录（EHR）包含患者随时间推移的健康信息，包括疾病的早期指标。然而，不断增加的数据量使得临床医生难以使用这些数据。有越来越多的证据表明，机器学习（ML）和深度学习（DL）可以帮助临床医生分析这些大规模的 EHR，因为算法在大量数据上表现出色。尽管 ML 已经发展得非常成熟，但研究主要集中在工程领域，缺乏医疗成果。
本研究旨在对使用 ML 分析纵向 EHR 以支持疾病的早期发现和预防的证据进行综述。通过回顾各种疾病中的应用，研究了生成的医学见解和临床获益。
本研究按照 PRISMA（系统评价和荟萃分析的首选报告项目）指南进行。 2022 年与一名医学信息专家合作在以下数据库中进行文献检索：PubMed、Embase、Web of Science 核心合集（Clarivate Analytics）和 IEEE Xplore 数字图书馆和计算机科学参考书目。当使用 ML 在预防环境中针对疾病的早期检测时，纵向 EHR 被用于研究时，研究才符合条件。本综述的范围不包括以技术为重点或使用影像学或住院数据的研究。研究筛选和选择以及数据提取由 2 名研究人员独立进行。
共纳入 20 项研究，主要发表于 2018 年至 2022 年期间。研究表明，许多疾病可以被检测或预测，特别是糖尿病、肾脏疾病、循环系统疾病以及精神、行为和神经发育障碍。在基本递归神经网络或长短期记忆技术中，人口统计学、症状、程序、实验室检测结果、诊断、药物和 BMI 是常用的 EHR 数据。通过开发和比较 ML 和 DL 模型，获得了一些医学见解，例如高诊断性能、更早的检测、最重要的预测因子和额外的健康指标。已评估为积极的临床获益是初步筛查。如果这些模型在实践中得到应用，患者也可能受益于个性化医疗保健和预防，具有减轻工作量和政策见解等实际效益。
纵向 EHR 已被证明有助于医疗保健。当前基于 EHR 的 ML 模型在准确性方面可以支持疾病的检测，并提供初步筛查的益处。在疾病预防方面，ML 模型，特别是 DL 模型可以比当前的临床诊断更早、更准确地预测或检测疾病。添加个人责任因素可以实现有针对性的预防干预。虽然基于文本的 EHR 的 ML 模型仍处于发展阶段，但它们具有支持临床医生和医疗保健系统并改善患者结局的巨大潜力。
