Evidence map›Paper›PMID 42448824›Full record

ArticleNPJ digital medicine2026

Transformer-DAPT: AI-based dynamic assessment of ischemic and bleeding risks in patients on DAPT following PCI.

Ahmed Abdelhameed, Zenan Sun, Yue Yu, Xinyue Hu, Demilade Adedinsewo, Jianping He, Yifang Dang, Jianfu Li, Jingna Feng, Haifang Li and 6 more

Abstract read
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In one paragraph

Article in NPJ digital medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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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

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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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

16 authors.

Ahmed Abdelhameed *Department of Artificial Intelligence and Informatics, Mayo Clinic, Jacksonville, FL, USA.
Zenan Sun *McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA.
Yue YuDepartment of Artificial Intelligence and Informatics, Mayo Clinic, Jacksonville, FL, USA.
Xinyue HuDepartment of Artificial Intelligence and Informatics, Mayo Clinic, Jacksonville, FL, USA.
Demilade AdedinsewoDepartment of Cardiovascular Medicine, Mayo Clinic, Jacksonville, FL, USA.
Jianping HeMcWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA.
Yifang DangMcWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA.
Jianfu LiDepartment of Artificial Intelligence and Informatics, Mayo Clinic, Jacksonville, FL, USA.
Jingna FengDepartment of Artificial Intelligence and Informatics, Mayo Clinic, Jacksonville, FL, USA.
Haifang LiDepartment of Artificial Intelligence and Informatics, Mayo Clinic, Jacksonville, FL, USA.
Laila RasmyMcWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA.
Michael GharacholouDepartment of Cardiovascular Medicine, Mayo Clinic, Jacksonville, FL, USA.
Degui ZhiMcWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA.
Jiang BianHealth Outcomes and Biomedical Informatics, University of Florida Health, Gainesville, FL, USA.
Fang LiDepartment of Artificial Intelligence and Informatics, Mayo Clinic, Jacksonville, FL, USA. li.fang@mayo.edu.
Cui TaoDepartment of Artificial Intelligence and Informatics, Mayo Clinic, Jacksonville, FL, USA. tao.cui@mayo.edu.

Funding

An end-to-end informatics framework to study Multiple Chronic Conditions (MCC)'s impact on Alzheimer's disease using harmonized electronic health recordsR01AG083039 · NIA · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI BIAN, JIANG, JIANG, XIAOQIAN · 2023 to 2025
$3.4M
Deep Learning Based Pharmacokinetic Model for VancomycinR01AI175699 · NIAID · METHODIST HOSPITAL RESEARCH INSTITUTE · PI NIGO, MASAYUKI · 2023 to 2025
$2.4M
Standardizing and Harmonizing Behavioral and Social Science Research Factors in Alzheimer's Disease through Ontology-Based ApproachesU01AG088076 · NIA · MAYO CLINIC JACKSONVILLE · PI Jiang Bian, Cui Tao · 2024 to 2026
$2.3M
American Heart Association 19GPSGC35180031National Institute of Allergy and Infectious Diseases R01AI175699NIAID NIH HHS R01 AI175699NIA NIH HHS R01 AG083039NIA NIH HHS R01AG083039NIA NIH HHS U01 AG088076
6 · The paper itself

Abstract

Dual antiplatelet therapy (DAPT) following percutaneous coronary intervention (PCI) is traditionally guided by rule-based scores providing static, single-time-point or fixed time-interval estimates with modest discrimination (C-index 0.63-0.73), limiting individualized DAPT duration decisions. We developed Transformer-DAPT, a transformer-based deep learning survival framework designed to estimate patient-specific ischemic and bleeding risks across clinically relevant time intervals during the first year after PCI. Using electronic health records from 29,032 patients at Mayo Clinic and externally validated in 19,173 patients from the OneFlorida+ Clinical Research Consortium, Transformer-DAPT achieved time-dependent concordance indices (C

Identifiers

PMID42448824
PMCPMC13653921

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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.