Modeling of time series health data using dynamic Bayesian networks: an application to predictions of patient outcomes after multiple surgeries

Objective: To develop dynamic predictive models for real-time outcome predictions of hospitalised patients. Design: Dynamic Bayesian networks (DBNs) were built to model patient outcomes that dynamically depend on patient's clinical profiles, temporal patterns of ward transfers and surgery data. These models were applied to predict remaining days of hospitalisation (RDH) for patients undergoing multiple surgeries and their performance compared against a static model based on Bayesian networks (BNs). Datasets: Hospital data from a Sydney metropolitan hospital. Results: The basic model uses static information at time of prediction. The DBN model uses static and temporal information extracted from a series of surgeries; DBNs show a significant improvement in patient outcome predictions with respect to the static model. Conclusion: Time series health data can be dynamically modelled by DBNs to improve predictions of outcomes for patients undergoing multiple surgeries.