Evidence map›Paper›PMID 35603127›Full record

ArticleNature machine intelligence2021

A deep learning framework for drug repurposing via emulating clinical trials on real-world patient data.

Ruoqi Liu, Lai Wei, Ping Zhang

Open access · greenAbstract read
In one paragraph

Article in Nature machine intelligence, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
50citing papers in PubMed, 1 pooled it
15.6field-weighted citation impact, top 1% 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

50 citing papers in PubMed, 1 synthesis or guideline pooled it, 106 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Target identification and assessment in the era of AI.Nature reviews. Drug discovery · 2026
    Review
  4. Article
  5. Article
  6. Review
  7. Review
  8. REACT (Real-world Emulated Clinical Trials): A Framework for Drug Repurposing Applied to Osteoporotic Fractures.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026
    Article
  9. Review
  10. Article
  11. Review
  12. Article
  13. Article
  14. Article
  15. Article
  16. Review
  17. A Deep Subgrouping Framework for Precision Drug Repurposing via Emulating Clinical Trials on Real-world Patient Data.KDD : proceedings. International Conference on Knowledge Discovery & Data Mining · 2025
    Article
  18. Article
  19. Article
  20. Article
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

3 authors at 1 institution in 1 country.

Ruoqi LiuDepartment of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA.
Lai WeiDepartment of Biomedical Informatics, The Ohio State University, Columbus, OH, USA.
Ping ZhangDepartment of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA.
The Ohio State University · US

Funding

The OSU Center for Clinical and Translational Science: Advancing Today's Discoveries to Improve HealthUL1TR002733 · NCATS · OHIO STATE UNIVERSITY · PI RINGEL, MATTHEW D · 2018 to 2022
$28.9M
NCATS NIH HHS UL1 TR002733
6 · The paper itself

Abstract

Drug repurposing is an effective strategy to identify new uses for existing drugs, providing the quickest possible transition from bench to bedside. Real-world data, such as electronic health records and insurance claims, provide information on large cohorts of users for many drugs. Here we present an efficient and easily customized framework for generating and testing multiple candidates for drug repurposing using a retrospective analysis of real-world data. Building upon well-established causal inference and deep learning methods, our framework emulates randomized clinical trials for drugs present in a large-scale medical claims database. We demonstrate our framework on a coronary artery disease cohort of millions of patients. We successfully identify drugs and drug combinations that substantially improve the coronary artery disease outcomes but haven't been indicated for treating coronary artery disease, paving the way for drug repurposing.

Identifiers

PMID35603127
PMCPMC9119409
OpenAlexW3120049458

What OpenQuestion holds

Textmetadata
LicenceTDM
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.