The Use of Deep Learning and Machine Learning on Longitudinal Electronic Health Records for the Early Detection and Prevention of Diseases: Scoping Review

Laura Swinckels*, Frank C. Bennis, Kirsten A. Ziesemer, Janneke F.M. Scheerman, Harmen Bijwaard, Ander de Keijzer, Josef Jan Bruers

*Corresponding author for this work

Research output: Contribution to JournalReview articleAcademicpeer-review

Abstract

Background: 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. Objective: 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. Methods: 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. Results: 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. Conclusions: 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.

Original languageEnglish
Article numbere48320
Pages (from-to)1-17
Number of pages17
JournalJournal of Medical Internet Research
Volume26
Early online date20 Aug 2024
DOIs
Publication statusPublished - 2024

Bibliographical note

Publisher Copyright:
©Laura Swinckels, Frank C Bennis, Kirsten A Ziesemer, Janneke F M Scheerman, Harmen Bijwaard, Ander de Keijzer, Josef Jan Bruers.

Funding

The first author conducted this study as part of her PhD trajectory. Her PhD trajectory was funded by the Centre of Expertise Prevention in Care and Wellbeing from Inholland University of Applied Sciences. JS acknowledges financial support from Regieorgaan SIA RAAK, part of the Netherlands Organisation for Scientific Research (grant HBOPD.2018.05.016). The remaining authors declare no other external sources of funding for this scoping review.

FundersFunder number
InHolland University of Applied Sciences
Centre of Expertise Prevention in Care
Regieorgaan SIA RAAK
Nederlandse Organisatie voor Wetenschappelijk OnderzoekHBOPD.2018.05.016

    Keywords

    • artificial intelligence
    • big data
    • detection
    • electronic health records
    • machine learning
    • personalized health care
    • prediction
    • prevention

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