Evidence map›Paper›PMID 35701544›Full record

ArticleNPJ digital medicine2022

Computational drug repurposing based on electronic health records: a scoping review.

Nansu Zong, Andrew Wen, Sungrim Moon, Sunyang Fu, Liwei Wang, Yiqing Zhao, Yue Yu, Ming Huang, Yanshan Wang, Gang Zheng and 3 more

Open access · goldAbstract readScoping Review
In one paragraph

Article in NPJ digital medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed, 1 pooled it
7.3field-weighted citation impact, top 2% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

27 citing papers in PubMed, 1 synthesis or guideline pooled it, 59 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors at 5 institutions in 1 country.

Nansu ZongDepartment of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, MN, USA. Zong.Nansu@mayo.edu.
Andrew WenDepartment of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, MN, USA.ORCID http://orcid.org/0000-0001-9090-8028
Sungrim MoonDepartment of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, MN, USA.ORCID http://orcid.org/0000-0002-9191-3897
Sunyang FuDepartment of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, MN, USA.ORCID http://orcid.org/0000-0003-1691-5179
Liwei WangDepartment of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, MN, USA.
Yiqing ZhaoDepartment of Preventive Medicine, Northwestern Medicine, Northwestern University, Chicago, IL, USA.
Yue YuDepartment of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, MN, USA.
Ming HuangDepartment of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, MN, USA.
Yanshan WangDepartment of Health Information Management, School of Health and Rehabilitation Sciences, University of Pittsburgh, Pittsburgh, PA, USA.
Gang ZhengDepartment of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA.
Michelle M MielkeWake Forest University School of Medicine, Winston-Salem, NC, USA.ORCID http://orcid.org/0000-0001-7177-1185
James R CerhanDepartment of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, MN, USA.ORCID http://orcid.org/0000-0002-7482-178X
Hongfang LiuDepartment of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, MN, USA.
Mayo Clinic in Florida · USMayo Clinic in Arizona · USNorthwestern University · USUniversity of Pittsburgh · USWake Forest University · US

Funding

BOND: Benchmarking based on heterogeneous biOmedical Network and Deep learning novel drug-target associationsR00GM135488 · NIGMS · MAYO CLINIC ROCHESTER · PI ZONG, NANSU · 2021 to 2023
$747k
BOND: Benchmarking based on heterogeneous biOmedical Network and Deep learning novel drug-target associationsK99GM135488 · NIGMS · MAYO CLINIC ROCHESTER · PI ZONG, NANSU · 2020 to 2021
$108k
NIGMS NIH HHS K99 GM135488NIGMS NIH HHS R00 GM135488U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS) K99GM135488
6 · The paper itself

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.

Identifiers

PMID35701544
PMCPMC9198008
OpenAlexW4282932819

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.