Impact of wearable device data and multi-scale entropy analysis on improving hospital readmission prediction.

ABSTRACT

OBJECTIVE: Unplanned readmissions following a hospitalization remain common despite significant efforts to curtail these. Wearable devices may offer help identify patients at high risk for an unplanned readmission. MATERIALS AND METHODS: We conducted a multi-center retrospective cohort study using data from the All of Us data repository. We included subjects with wearable data and developed a baseline Feedforward Neural Network (FNN) model and a Long Short-Term Memory (LSTM) time-series deep learning model to predict daily, unplanned rehospitalizations up to 90 days from discharge. In addition to demographic and laboratory data from subjects, post-discharge data input features include wearable data and multiscale entropy features based on intraday wearable time series. The most significant features in the LSTM model were determined by permutation feature importance testing. RESULTS: In sum, 612 patients met inclusion criteria. The complete LSTM model had a higher area under the receiver operating characteristic curve than the FNN model (0.83 vs 0.795). The 5 most important input features included variables from multiscale entropy (steps) and number of active steps per day. DISCUSSION: Data available from wearable devices can improve ability to predict readmissions. Prior work has focused on predictors available up to discharge or on additional data abstracted from wearable devices. Our results from 35 institutions highlight how multiscale entropy can improve readmission prediction and may impact future work in this domain. CONCLUSION: Wearable data and multiscale entropy can improve prediction of a deep-learning model to predict unplanned 90-day readmissions. Prospective studies are needed to validate these findings.