Deep-learning-based prognostic modeling for incident heart failure in patients with diabetes using electronic health records: A retrospective cohort study

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 fac...

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Veröffentlicht in:	PloS one 2023-02, Vol.18 (2), p.e0281878-e0281878
Hauptverfasser:	Gandin, Ilaria, Saccani, Sebastiano, Coser, Andrea, Scagnetto, Arjuna, Cappelletto, Chiara, Candido, Riccardo, Barbati, Giulia, Di Lenarda, Andrea
Format:	Artikel
Sprache:	eng
Schlagworte:	Analysis Artificial Intelligence Artificial neural networks Blood pressure Body mass Body mass index Body size Calibration Cardiovascular disease Care and treatment Cohort analysis Comorbidity Computer and Information Sciences Congestive heart failure Creatinine Deep Learning Diabetes Diabetes mellitus Diabetes mellitus (non-insulin dependent) Diabetes Mellitus, Type 2 Diabetics Diagnosis Distribution Electronic Health Records Electronic medical records Electronic records Feature extraction Health aspects Health care Health hazards Health risks Health services Heart Failure Humans Hypotheses Machine learning Medical prognosis Medical records Medicine and Health Sciences Methods Modelling Neural networks Observatories Patients Prevention Prognosis Regularization Retrospective Studies Risk Risk Factors Statistical models Survival Survival analysis
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