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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Davis, K.: 2012 Annual Report: President’s Message—Health Care Reform: A Journey. Commonwealth Fund, New York (2012)
Cusack, C.M., Hripcsak, G., Bloomrosen, M., Rosenbloom, S.T., Weaver, C.A., Wright, A., et al.: The future state of clinical data capture and documentation: a report from AMIA’s 2011 policy meeting. J. Am. Med. Inform. Assoc. 20(1), 134–140 (2013)
Billings, J., Georghiou, T., Blunt, I., et al.: Choosing a model to predict hospital admission: an observational study of new variants of predictive models for case finding. BMJ Open 3, e003352 (2013). doi:10.1136/bmjopen-2013-003352
Zhou, J., Shepherd, M., Li, F., Fu, L., et al.: Delivering actionable insights on population risk to improve health outcomes and reduce healthcare costs. In: International Conference on Health Informatics and Medical Systems (2015)
Kartoun, U., Kumar, V., Brettman, A., Yu, S., Liao, K., et al.: A risk model for 30-day heart failure readmission using electronic medical records. In: 2015 Joint Summits on Translational Science, San Francisco, CA, March 2015
Krishnan, R., Razavian, N., Choi, Y., Nigam, S., Blecker, S., Schmidt, A., Sontag, D.: Early detection of diabetes from health claims. In: Machine Learning in Healthcare Workshop, NIPS (2013)
Bhattacharya, S., Rajan, V., Huddar, V.: A novel classification method for predicting acute hypotensive episodes in critical care. In: 5th ACM Conference on Bioinformatics, Computational Biology and Health Informatics (ACM BCB 2014), Newport Beach, USA (2014)
Somanchi, S., Adhikari, S., Lin, A., Eneva, E., Ghani, R.: Early prediction of cardiac arrest (code blue) using electronic medical records. In: 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2015)
Kartoun, U., Corey, K., Zheng, H., Shaw, S.: A prediction model to assess mortality risk in cirrhosis. In: 2016 Joint Summits on Translational Science, San Francisco, CA, March 2016
Gelman, A., Hill, J.: Data Analysis Using Regression and Multi-level/Hierarchical Models, 1st edn. Cambridge University Press, Cambridge (2006)
Kansagara, D., Englander, H., Salanitro, A., Kagen, D., et al.: Risk prediction models for hospital readmission: a systematic review. JAMA 306(15), 1688–1698 (2011)
Walraven, C.V., Wong, J., Forster, A.J.: LACE+ index: extension of a validated index to predict early death or urgent readmission after hospital dischargeusing administrative data. Open Med. 6(3), e80–90 (2012)
Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001)
Ishwaran, H., Kogalur, U., Blackstone, E., Lauer, M.: Random survival forestss. Ann. Appl. Stat. 2(3), 841–860 (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-42996-0_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-42995-3
Online ISBN: 978-3-319-42996-0
eBook Packages: Computer ScienceComputer Science (R0)