Improving the Prognostic Evaluation Precision of Hospital Outcomes for Heart Failure Using Admission Notes and Clinical Tabular Data: Multimodal Deep Learning Model.
Zhenyue GaoXiaoli LiuYu KangPan HuXiu ZhangWei YanMuyang YanPengming YuQing ZhangWendong XiaoZhengbo ZhangPublished in: Journal of medical Internet research (2024)
The multimodal deep learning model for combining admission notes and clinical tabular data showed promising efficacy as a potentially novel method in evaluating the risk of mortality in patients with HF, providing more accurate and timely decision support.
Keyphrases
- deep learning
- heart failure
- emergency department
- electronic health record
- big data
- artificial intelligence
- pain management
- machine learning
- healthcare
- convolutional neural network
- cardiovascular events
- left ventricular
- risk factors
- atrial fibrillation
- acute heart failure
- metabolic syndrome
- coronary artery disease
- adverse drug