Modeling of time series health data using Dynamic Bayesian Networks: An application to predictions of patient outcomes after multiple surgeries

S : scientific #bd14 | big data conference 22 Dr Xiongcai (Peter) Cai Research Fellow Centre for Health Informatics, The University of New South Wales Dr Xiongcai (Peter) Cai is a researcher with a general background in AI with expertise in the fields of machine learning, data mining, social network analysis and health informatics. He has been spending the past decade researching and developing models to better understand behaviours and patterns that relate real world human activities. Modeling of time series health data using Dynamic Bayesian Networks: An application to predictions of patient outcomes after multiple surgeries Xiongcai Cai, Oscar Perez-Concha, Fernando Martin-Sanchez, Blanca Gallego a Centre for Health Informatics, Australian Institute of Health Innovation, University of New South Wales, NSW 2052 b Health and Biomedical Informatics Centre, University of Melbourne, Victoria 3010 SUMMARY 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. INTRODUCTION Healthcare systems are under increasing pressure to identify strategies to improve the current patterns of care. Although unstructured data analytics have been widely reported in big data, the importance of time series has not been fully explored yet. Real-time prediction of patient outcomes could benefit greatly from big data in health and time series analysis. In particular, prediction of RDH is an important indicator to assess healthcare delivery and hospital management. Unexpectedly long length of stay may negatively impact patients and hospitals in a variety of ways, such as higher costs and increased exposure to adverse events. Current methods do not allow real-time automated stratification of risk. Rapidly identifying those patients at highest risk of extended RDH has a great potential to improve the quality of care, reduce avoidable harm and costs. DBNs are specially suitable to tackle this problem, since they are probabilistic graphical models that allow temporal order, which can better capture the dynamical nature of the healthcare delivery processes: prognosis, treatment selection, surgery and recovery. In this paper, we aim to develop a DBNs-based prediction model to investigate the roles of time series data for the prediction of RDH for patients undergoing multiple surgeries. DESCRIPTION Medical records of patients who underwent consecutive surgeries at a Sydney metropolitan hospital between 2008-2012 were analysed. There are 5733 records in the dataset. Each admission is characterised by a set of attributes, which include: patient’s characteristics (such as age), surgery information (main procedure, number of procedures, length of surgery) and ward type. These attributes, together with days already in hospital, constitute the inputs to the static BN. The outcome to be predicted is RDH. BNs are static probabilistic graphical models that consists of nodes and arcs forming a directed acyclic graph, where nodes present domain variables (predictors), whereas arcs represent conditional probabilistic relationships among variables. DBNs represent the evolution of a system over time, allowing a fixed structure network to present variables at multiple time points (slices), containing temporal dependencies between slices. We learned both structures and model parameters: 1) In order to construct the static BNs (Figure 1), we learned intra-slice structures and reinforced them with domain knowledge. These BNs will be the baselines to compare with the DBNs; at the point of prediction, the BN will contain the information of the current [email protected]