Predicting heart failure outcomes by integrating breath-by-breath measurements from cardiopulmonary exercise testing and clinical data through a deep learning survival neural network.
Heather Joan RossMohammad PeikariJulie K K Vishram-NielsenChun-Po S FanJason HearnMike WalkerEdgar CrowdyAna Carolina AlbaCedric ManlhiotPublished in: European heart journal. Digital health (2024)
Using a combined deep learning and survival algorithm, integrating breath-by-breath data from CPETs resulted in improved predictive accuracy for long-term (up to 10 years) outcomes in HF. DeepSurv opens the door for future prediction models that are both highly performing and can more fully use the large and complex quantity of data generated during the care of patients with HF.
Keyphrases
- deep learning
- neural network
- heart failure
- electronic health record
- big data
- machine learning
- artificial intelligence
- healthcare
- acute heart failure
- convolutional neural network
- physical activity
- high intensity
- type diabetes
- adipose tissue
- left ventricular
- metabolic syndrome
- atrial fibrillation
- resistance training
- weight loss
- health insurance