ArticleIEEE journal of biomedical and health informatics2024
Multi-Branching Temporal Convolutional Network With Tensor Data Completion for Diabetic Retinopathy Prediction.
Article in IEEE journal of biomedical and health informatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it, 14 citations in OpenAlex.
- Artificial Intelligence in Cerebrovascular Disease Management: A Comprehensive Review of Risk Prediction, Diagnosis, Therapeutic Optimization, and Clinical Translation.Vascular health and risk management · 2025Pooled it
- Evaluation of trajectory analysis for disease risk assessment: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- MUSE-Net: Missingness-aware mUlti-branching Self-attention Encoder for Irregular Longitudinal Electronic Health Records.IEEE transactions on automation science and engineering : a publication of the IEEE Robotics and Automation Society · 2026Article
- Monte Carlo Tree Search for optimal cancer intervention strategies among BRCA mutation carriers.Computers in biology and medicine · 2025Article
- D-TNet: a hybrid Dense Net-transformer model for robust diabetic retinopathy detection.Scientific reports · 2025Article
- Multi-Source Data and Knowledge Fusion via Deep Learning for Dynamical Systems: Applications to Spatiotemporal Cardiac Modeling.IISE transactions on healthcare systems engineering · 2025Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
Abstract
Diabetic retinopathy (DR), a microvascular complication of diabetes, is the leading cause of vision loss among working-aged adults. However, due to the low compliance rate of DR screening and expensive medical devices for ophthalmic exams, many DR patients did not seek proper medical attention until DR develops to irreversible stages (i.e., vision loss). Fortunately, the widely available electronic health record (EHR) databases provide an unprecedented opportunity to develop cost-effective machine-learning tools for DR detection. This paper proposes a Multi-branching Temporal Convolutional Network with Tensor Data Completion (MB-TCN-TC) model to analyze the longitudinal EHRs collected from diabetic patients for DR prediction. Experimental results demonstrate that the proposed MB-TCN-TC model not only effectively copes with the imbalanced data and missing value issues commonly seen in EHR datasets but also captures the temporal correlation and complicated interactions among medical variables in the longitudinal clinical records, yielding superior prediction performance compared to existing methods. Specifically, our MB-TCN-TC model provides AUROC and AUPRC scores of 0.949 and 0.793 respectively, achieving an improvement of 6.27% on AUROC, 11.85% on AUPRC, and 19.3% on F1 score compared with the traditional TCN model.
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Registered trials
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.