  Objective: Peritoneal Dialysis (PD) is one of the most widely used
life-supporting therapies for patients with End-Stage Renal Disease (ESRD).
Predicting mortality risk and identifying modifiable risk factors based on the
Electronic Medical Records (EMR) collected along with the follow-up visits are
of great importance for personalized medicine and early intervention. Here, our
objective is to develop a deep learning model for a real-time, individualized,
and interpretable mortality prediction model - AICare. Method and Materials:
Our proposed model consists of a multi-channel feature extraction module and an
adaptive feature importance recalibration module. AICare explicitly identifies
the key features that strongly indicate the outcome prediction for each patient
to build the health status embedding individually. This study has collected
13,091 clinical follow-up visits and demographic data of 656 PD patients. To
verify the application universality, this study has also collected 4,789 visits
of 1,363 hemodialysis dialysis (HD) as an additional experiment dataset to test
the prediction performance, which will be discussed in the Appendix. Results:
1) Experiment results show that AICare achieves 81.6%/74.3% AUROC and
47.2%/32.5% AUPRC for the 1-year mortality prediction task on PD/HD dataset
respectively, which outperforms the state-of-the-art comparative deep learning
models. 2) This study first provides a comprehensive elucidation of the
relationship between the causes of mortality in patients with PD and clinical
features based on an end-to-end deep learning model. 3) This study first
reveals the pattern of variation in the importance of each feature in the
mortality prediction based on built-in interpretability. 4) We develop a
practical AI-Doctor interaction system to visualize the trajectory of patients'
health status and risk indicators.
