ArticleiScience2024
Risk prediction of heart diseases in patients with breast cancer: A deep learning approach with longitudinal electronic health records data.
Article in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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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.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning in the diagnosis and prognosis of transient ischaemic attack: a systematic review.BMC neurology · 2026Pooled it
- From data to decision: a clinical pipeline for wearable AI in early cardiotoxicity detection.Cardio-oncology (London, England) · 2026Review
- Toward accurate prediction of pediatric epidemic disease patient volume in the Chaoshan region: A deep learning framework.iScience · 2026Article
- Multimodal fusion of EHR and ECG based on deep learning for predicting new-onset coronary heart disease in cancer patients.Frontiers in cardiovascular medicine · 2026Article
- AI and Smart Devices in Cardio-Oncology: Advancements in Cardiotoxicity Prediction and Cardiovascular Monitoring.Diagnostics (Basel, Switzerland) · 2025Review
- Multi-modality risk prediction of cardiovascular diseases for breast cancer cohort in the All of Us Research Program.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Mortality Prediction Modeling for Patients with Breast Cancer Based on Explainable Machine Learning.Cancers · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Accurately predicting heart disease risks in patients with breast cancer is crucial for clinical decision support and patient safety. This study developed and evaluated predictive models for six heart diseases using real-world electronic health records (EHRs) data. We incorporated a trainable decay mechanism to handle missing values in the long short-term memory (LSTM) model, creating LSTM-D models to predict heart disease risk based on longitudinal EHRs data. Additionally, we deployed NLP methods to extract breast cancer phenotypes from clinical texts, integrating unstructured and structured data to enhance predictions. Our LSTM-D models outperformed baseline models in predicting congestive heart failure, coronary artery disease, cardiomyopathy, myocardial infarction, transient ischemic attack, and aortic regurgitation, with AUC scores ranging from 0.7189 to 0.9548. Observation windows of 12-24 months were found optimal for model performance. This research advances precise, personalized care strategies, enabling early intervention and improved management of cardiovascular risks in breast cancer survivors.
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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.