ArticlePloS one2023
Deep-learning-based prognostic modeling for incident heart failure in patients with diabetes using electronic health records: A retrospective cohort study.
Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 3 of them syntheses that pooled it.
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Who cites it
11 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Diagnostic Performance of Machine Learning Algorithms for Predicting Heart Failure in Diabetic Patients: A Systematic Review and Meta-Analysis.Endocrinology, diabetes & metabolism · 2025Pooled it
- Pooled it
- A Systematic Review of Artificial Intelligence Models for Time-to-Event Outcome Applied in Cardiovascular Disease Risk Prediction.Journal of medical systems · 2024Pooled it
- Artificial Intelligence to Diagnose Complications of Diabetes.Journal of diabetes science and technology · 2025Review
- Unveiling diabetes onset: Optimized XGBoost with Bayesian optimization for enhanced prediction.PloS one · 2025Article
- Machine learning and deep learning in diabetology: revolutionizing diabetes care.Frontiers in clinical diabetes and healthcare · 2025Article
- Leveraging calcium score CT radiomics for heart failure risk prediction.Scientific reports · 2024Article
- Machine learning in heart failure diagnosis, prediction, and prognosis: review.Annals of medicine and surgery (2012) · 2024Review
- A review of evaluation approaches for explainable AI with applications in cardiology.Artificial intelligence review · 2024Article
- Application of machine learning in predicting survival outcomes involving real-world data: a scoping review.BMC medical research methodology · 2023Article
- Machine learning in precision diabetes care and cardiovascular risk prediction.Cardiovascular diabetology · 2023Review
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Patients with type 2 diabetes mellitus (T2DM) have more than twice the risk of developing heart failure (HF) compared to patients without diabetes. The present study is aimed to build an artificial intelligence (AI) prognostic model that takes in account a large and heterogeneous set of clinical factors and investigates the risk of developing HF in diabetic patients. We carried out an electronic health records- (EHR-) based retrospective cohort study that included patients with cardiological clinical evaluation and no previous diagnosis of HF. Information consists of features extracted from clinical and administrative data obtained as part of routine medical care. The primary endpoint was diagnosis of HF (during out-of-hospital clinical examination or hospitalization). We developed two prognostic models using (1) elastic net regularization for Cox proportional hazard model (COX) and (2) a deep neural network survival method (PHNN), in which a neural network was used to represent a non-linear hazard function and explainability strategies are applied to estimate the influence of predictors on the risk function. Over a median follow-up of 65 months, 17.3% of the 10,614 patients developed HF. The PHNN model outperformed COX both in terms of discrimination (c-index 0.768 vs 0.734) and calibration (2-year integrated calibration index 0.008 vs 0.018). The AI approach led to the identification of 20 predictors of different domains (age, body mass index, echocardiographic and electrocardiographic features, laboratory measurements, comorbidities, therapies) whose relationship with the predicted risk correspond to known trends in the clinical practice. Our results suggest that prognostic models for HF in diabetic patients may improve using EHRs in combination with AI techniques for survival analysis, which provide high flexibility and better performance with respect to standard approaches.
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