Yuanyuan Zheng, Adel Bensahla, Mina Bjelogrlic, Jamil Zaghir, Hugues Turbe, Lydie Bednarczyk, Christophe Gaudet-Blavignac, Julien Ehrsam, Stéphane Marchand-Maillet, Christian Lovis
Division of Medical Information Sciences, Geneva University Hospitals, Geneva, Switzerland.
Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland.
NPJ Digit Med. 2025 Jun 14;8(1):362. doi: 10.1038/s41746-025-01692-1.
The widespread adoption of Electronic Health Records (EHRs) and deep learning, particularly through Self-Supervised Representation Learning (SSRL) for categorical data, has transformed clinical decision-making. This scoping review, following PRISMA-ScR guidelines, examines 46 studies published from January 2019 to April 2024, sourced from PubMed, MEDLINE, Embase, ACM, and Web of Science, focusing on SSRL for unlabeled categorical EHR data. The review systematically assesses research trends in building computationally and data-efficient representations for medical tasks, identifying major trends in model families: Transformer-based (43%), Autoencoder-based (28%), and Graph Neural Network-based (17%) models. The analysis highlights scenarios where healthcare institutions can leverage or develop SSRL technologies. It also addresses current limitations in assessing the impact of these technologies and identifies research opportunities to enhance their influence on clinical practice.
电子健康记录（EHRs）和深度学习的广泛应用，特别是通过针对分类数据的自监督表示学习（SSRL），已经改变了临床决策。本范围综述遵循PRISMA-ScR指南，审查了2019年1月至2024年4月发表的46项研究，这些研究来自PubMed、MEDLINE、Embase、ACM和科学网，重点关注针对未标记分类EHR数据的SSRL。该综述系统地评估了为医疗任务构建计算和数据高效表示的研究趋势，确定了模型家族的主要趋势：基于Transformer的模型（43%）、基于自动编码器的模型（28%）和基于图神经网络的模型（17%）。分析突出了医疗机构可以利用或开发SSRL技术的场景。它还解决了评估这些技术影响时的当前局限性，并确定了增强其对临床实践影响的研究机会。
