JMIR Publications

ABSTRACT

Background:

In-hospital cardiac arrest (IHCA) is associated with high mortality and healthcare costs in the recovery phase. Predicting adverse outcome events including readmission provides the chance for appropriate interventions and reducing healthcare costs. However, studies related to the early prediction of adverse events of IHCA survivors are rare. A deep learning model was used for prediction in this study.

Objective:

This study aimed to demonstrate that with the proper dataset and learning strategies, we can predict 30-day mortality and readmission of IHCA survivors based on their historical claims.

Methods:

National Health Insurance Research Database claims data, including 168,693 patients who had experienced IHCA at least one time and 1,569,478 clinical records, were obtained to generate a dataset for outcome prediction. We predicted the 30-day mortality/readmission after each current record (ALL-mortality/ALL-readmission) and 30-day mortality/readmission after IHCA (CA-mortality/CA-readmission). We developed a hierarchical vectorizer (HVec) deep learning model to extract patients’ information and predict mortality and readmission. To embed the textual medical concepts of the clinical records into our deep learning model, we used Text2Node to compute the distributed representations of all medical concept codes as a 128-dimensional vector. Along with the patient’s demographic information, our novel HVec model generated embedding vectors to describe the health status at the record level and patient level hierarchically. Multi-task learning involving two main tasks and auxiliary tasks was proposed. As CA-mortality and CA-readmission were rare, person upsampling of patients with CA and weighting of CA records were used to improve prediction performance.

Results:

With the multi-task learning setting in the model learning process, we achieved an AUROC of 0.752 for CA-mortality, 0.711 for ALL-mortality, 0.852 for CA-readmission, and 0.889 for ALL-readmission. The AUROC was improved to 0.808 for CA-mortality and 0.862 for CA-readmission after solving the extremely imbalanced issue for CA-mortality/CA-readmission by upsampling and weighting.

Conclusions:

This study demonstrated the potential of predicting future outcomes for IHCA survivors by machine learning. The results showed that our proposed approach could effectively alleviate data imbalance problems and train a better model for outcome prediction.