Computational drug repurposing methods adapt Artificial intelligence (AI) algorithms for the discovery of new applications of approved or investigational drugs. Among the heterogeneous datasets, electronic health records (EHRs) datasets provide rich longitudinal and pathophysiological data that facilitate the generation and validation of drug repurposing. Here, we present an appraisal of recently published research on computational drug repurposing utilizing the EHR. Thirty-three research articles, retrieved from Embase, Medline, Scopus, and Web of Science between January 2000 and January 2022, were included in the final review. Four themes, (1) publication venue, (2) data types and sources, (3) method for data processing and prediction, and (4) targeted disease, validation, and released tools were presented. The review summarized the contribution of EHR used in drug repurposing as well as revealed that the utilization is hindered by the validation, accessibility, and understanding of EHRs. These findings can support researchers in the utilization of medical data resources and the development of computational methods for drug repurposing.

计算药物再利用方法采用人工智能 (AI) 算法来发现已批准或研究药物的新应用。在异构数据集中，电子健康记录 (EHR) 数据集提供了丰富的纵向和病理生理学数据，有助于药物再利用的生成和验证。在这里，我们对最近发表的利用 EHR 计算药物再利用的研究进行了评估。2000 年 1 月至 2022 年 1 月期间从 Embase、Medline、Scopus 和 Web of Science 检索到的 33 篇研究文章被纳入最终审查。介绍了四个主题，(1) 出版地点，(2) 数据类型和来源，(3) 数据处理和预测方法，以及 (4) 目标疾病、验证和发布的工具。审查总结了 EHR 在药物再利用中的贡献，并揭示了 EHR 的验证、可访问性和理解阻碍了利用。这些发现可以支持研究人员利用医学数据资源和开发药物再利用的计算方法。