Evidence map›Paper›PMID 39994365›Full record

Observational studyScientific reports2025

Natural language processing of electronic medical records identifies cardioprotective agents for anthracycline induced cardiotoxicity.

Yoshimasa Kawazoe, Masami Tsuchiya, Kiminori Shimamoto, Tomohisa Seki, Emiko Shinohara, Shuntaro Yada, Shoko Wakamiya, Shungo Imai, Eiji Aramaki, Satoko Hori

Abstract readObservational Study
PubMed Full text at PMC Publisher
In one paragraph

Observational study in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
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

2 citing papers in PubMed.

  1. Review
  2. 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

10 authors.

Yoshimasa KawazoeArtificial Intelligence and Digital Twin in Healthcare, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan. kawazoe@m.u-tokyo.ac.jp.
Masami TsuchiyaDivision of Drug Informatics, Keio University Faculty of Pharmacy, Tokyo, Japan.
Kiminori ShimamotoArtificial Intelligence and Digital Twin in Healthcare, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Tomohisa SekiDepartment of Healthcare Information Management, The University of Tokyo Hospital, Tokyo, Japan.
Emiko ShinoharaArtificial Intelligence and Digital Twin in Healthcare, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Shuntaro YadaDivision of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, Nara, Japan.
Shoko WakamiyaDivision of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, Nara, Japan.
Shungo ImaiDivision of Drug Informatics, Keio University Faculty of Pharmacy, Tokyo, Japan.
Eiji AramakiDivision of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, Nara, Japan.
Satoko HoriDivision of Drug Informatics, Keio University Faculty of Pharmacy, Tokyo, Japan.

Funding

Core Research for Evolutional Science and Technology JPMJCR22N1Japan Society for the Promotion of Science 23H03492Next Cross-ministerial Strategic Innovation Promotion Program (SIP) on "Integrated Health Care System" JPJ012425
6 · The paper itself

Abstract

In this retrospective observational study, we aimed to investigate the potential of natural language processing (NLP) for drug repositioning by analyzing the preventive effects of cardioprotective drugs against anthracycline-induced cardiotoxicity (AIC) using electronic medical records. We evaluated the effects of angiotensin II receptor blockers/angiotensin-converting enzyme inhibitors (ARB/ACEIs), beta-blockers (BBs), statins, and calcium channel blockers (CCBs) on AIC using signals extracted from clinical texts via NLP. The study included 2935 patients prescribed anthracyclines at a single hospital, with concomitant prescriptions of ARB/ACEIs, BBs, statins, and CCBs. Upon propensity score matching, groups with and without these medications were compared, and expressions suggestive of cardiotoxicity, extracted via NLP, were considered as the outcome. The hazard ratios for ARB/ACEIs, BBs, statins, and CCBs were 0.58 [95% CI: 0.38-0.88], 0.71 [95% CI: 0.35-1.44], 0.60 [95% CI 0.38-0.95], and 0.63 [95% CI: 0.45-0.88], respectively. ARB/ACEIs, statins, and CCBs significantly suppressed AIC, whereas BBs did not demonstrate statistical significance, possibly due to limited statistical power. NLP-extracted signals from clinical texts reflected the known effects of these medications, demonstrating the feasibility of NLP-based drug repositioning. Further investigation is needed to determine if similar results can be replicated using electronic medical records from other institutions.

Indexed as

AnthracyclinesCardiotonic AgentsCardiotoxicityElectronic Health RecordsNatural Language ProcessingAdrenergic beta-AntagonistsAdultAgedAngiotensin-Converting Enzyme InhibitorsAngiotensin Receptor AntagonistsCalcium Channel BlockersDrug RepositioningFemaleHumansHydroxymethylglutaryl-CoA Reductase InhibitorsMaleAdrenergic beta-AntagonistsAngiotensin-Converting Enzyme InhibitorsAngiotensin Receptor AntagonistsAnthracyclinesCalcium Channel BlockersCardiotonic AgentsHydroxymethylglutaryl-CoA Reductase InhibitorsAnthracycline-induced cardiotoxicityDrug repurposingElectronic medical recordsNatural Language processing

Identifiers

PMID39994365
PMCPMC11850854

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.