Recurrent disease progression networks for modelling risk trajectory of heart failure.
Xing Han LuAihua LiuShih-Chieh FuhYi LianLiming GuoYi YangAriane MarelliYue LiPublished in: PloS one (2021)
Our deep learning models confers higher prediction accuracy for both the next-step HF prediction and the HF trajectory prediction compared to the baseline non-neural network models and the baseline RNN model. Compared to DHTM, DHTM+C is able to output higher probability of HF for high-risk patients, even in cases where it is only given less than 2 years of data to predict over 5 years of trajectory. We illustrated multiple non-trivial real patient examples of complex HF trajectories, indicating a promising path for creating highly accurate and scalable longitudinal deep learning models for modeling the chronic disease.
Keyphrases
- deep learning
- acute heart failure
- heart failure
- neural network
- end stage renal disease
- newly diagnosed
- ejection fraction
- chronic kidney disease
- prognostic factors
- convolutional neural network
- depressive symptoms
- case report
- high resolution
- electronic health record
- atrial fibrillation
- left ventricular
- mass spectrometry