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Computational drug repurposing based on electronic health records: a scoping review.
Zong, Nansu; Wen, Andrew; Moon, Sungrim; Fu, Sunyang; Wang, Liwei; Zhao, Yiqing; Yu, Yue; Huang, Ming; Wang, Yanshan; Zheng, Gang; Mielke, Michelle M; Cerhan, James R; Liu, Hongfang.
  • Zong N; Department of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, MN, USA. Zong.Nansu@mayo.edu.
  • Wen A; Department of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, MN, USA.
  • Moon S; Department of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, MN, USA.
  • Fu S; Department of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, MN, USA.
  • Wang L; Department of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, MN, USA.
  • Zhao Y; Department of Preventive Medicine, Northwestern Medicine, Northwestern University, Chicago, IL, USA.
  • Yu Y; Department of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, MN, USA.
  • Huang M; Department of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, MN, USA.
  • Wang Y; Department of Health Information Management, School of Health and Rehabilitation Sciences, University of Pittsburgh, Pittsburgh, PA, USA.
  • Zheng G; Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA.
  • Mielke MM; Wake Forest University School of Medicine, Winston-Salem, NC, USA.
  • Cerhan JR; Department of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, MN, USA.
  • Liu H; Department of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, MN, USA.
NPJ Digit Med ; 5(1): 77, 2022 Jun 14.
Article in English | MEDLINE | ID: covidwho-1937453
ABSTRACT
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.

Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study / Reviews Language: English Journal: NPJ Digit Med Year: 2022 Document Type: Article Affiliation country: S41746-022-00617-6

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study / Reviews Language: English Journal: NPJ Digit Med Year: 2022 Document Type: Article Affiliation country: S41746-022-00617-6