Abstract
During the recent few years, the United States healthcare industry is under unprecedented pressure to improve outcome and reduce cost. Many healthcare organizations are leveraging healthcare analytics, especially predictive analytics in moving towards these goals and bringing better value to the patients. While many existing event prediction models provide helpful predictions in terms of accuracy, their use are typically limited to prioritizing individual patients for care management at fixed time points. In this paper we explore Enhanced Modeling approaches around two important aspects: (1) model interpretability; (2) flexible prediction window. Better interpretability of the model will guide us towards more effective intervention design. Flexible prediction window can provide a higher resolution picture of patients’ risks of adverse events over time, and thereby enable timely interventions. We illustrate interpretation and insights from our Bayesian Hierarchical Model for readmission prediction, and demonstrate flexible prediction window with Random Survival Forests model for prediction of future emergency department visits.
L. Fu and F. Li—Equal contribution.
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Fu, L., Li, F., Zhou, J., Wen, X., Yao, J., Shepherd, M. (2016). Event Prediction in Healthcare Analytics: Beyond Prediction Accuracy. In: Cao, H., Li, J., Wang, R. (eds) Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2016. Lecture Notes in Computer Science(), vol 9794. Springer, Cham. https://doi.org/10.1007/978-3-319-42996-0_15
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DOI: https://doi.org/10.1007/978-3-319-42996-0_15
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