Abstract
Assessing prognostic risk is crucial to clinical care, and critically dependent on both diagnosis and medical interventions. Current methods use this augmented information to build a single prediction rule. But this may not be expressive enough to capture differential effects of interventions on prognosis. To this end, we propose a supervised, Bayesian nonparametric framework that simultaneously discovers the latent intervention groups and builds a separate prediction rule for each intervention group. The prediction rule is learnt using diagnosis data through a Bayesian logistic regression. For inference, we develop an efficient collapsed Gibbs sampler. We demonstrate that our method outperforms baselines in predicting 30-day hospital readmission using two patient cohorts - Acute Myocardial Infarction and Pneumonia. The significance of this model is that it can be applied widely across a broad range of medical prognosis tasks.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Al-Sarraf, M., LeBlanc, M., Giri, P., Fu, K.K., Cooper, J., Vuong, T., Forastiere, A.A., Adams, G., Sakr, W.A., Schuller, D.E., Ensley, J.F.: Chemoradiotherapy versus radiotherapy in patients with advanced nasopharyngeal cancer: phase iii randomized intergroup study. Journal of Clinical Oncology 16(4), 1310–1317 (1998)
Hannan, E.L., Racz, M.J., Walford, G., Jones, R.H., Ryan, T.J., Bennett, E., Culliford, A.T., Isom, O.W., Gold, J.P., Rose, E.A.: Long-term outcomes of coronary-artery bypass grafting versus stent implantation. New England Journal of Medicine 352(21), 2174–2183 (2005)
Donzé, J., Aujesky, D., Williams, D., Schnipper, J.L.: Potentially avoidable 30-day hospital readmissions in medical patientsderivation and validation of a prediction modelpotentially avoidable 30-day hospital readmissions. JAMA Internal Medicine 173(8), 632–638 (2013)
Shahbaba, B., Neal, R.: Nonlinear models using dirichlet process mixtures. The Journal of Machine Learning Research 10, 1829–1850 (2009)
Jencks, S.F., Williams, M.V., Coleman, E.A.: Rehospitalizations among patients in the medicare fee-for-service program. New England Journal of Medicine 360(14), 1418–1428 (2009)
Bradley, E.H., Curry, L., Horwitz, L.I., Sipsma, H., Wang, Y., Walsh, M.N., Goldmann, D., White, N., Piña, I.L., Krumholz, H.M.: Hospital strategies associated with 30-day readmission rates for patients with heart failure. Circulation: Cardiovascular Quality and Outcomes 6(4), 444–450 (2013)
Omar Hasan, M., Meltzer, D.O., Shaykevich, S.A., Bell, C.M., Kaboli, P.J., Auerbach, A.D., Wetterneck, T.B., Arora, V.M., Schnipper, J.L.: Hospital readmission in general medicine patients: a prediction model. Journal of General Internal Medicine 25(3), 211–219 (2010)
van Walraven, C., Dhalla, I.A., Bell, C., Etchells, E., Stiell, I.G., Zarnke, K., Austin, P.C., Forster, A.J.: Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community. Canadian Medical Association Journal 182(6), 551–557 (2010)
Meadem, N., Verbiest, N., Zolfaghar, K., Agarwal, J., Chin, S.-C., Roy, S.B.: Exploring preprocessing techniques for prediction of risk of readmission for congestive heart failure patients (2013)
Cholleti, S., Post, A., Gao, J., Lin, X., Bornstein, W., Cantrell, D., Saltz, J.: Leveraging derived data elements in data analytic models for understanding and predicting hospital readmissions, vol. 2012, 103 (2012)
Ferguson, T.S.: A bayesian analysis of some nonparametric problems. The Annals of Statistics, 209–230 (1973)
Sethuraman, J.: A constructive definition of dirichlet priors. DTIC Document, Tech. Rep. (1991)
Pitman, J.: Combinatorial stochastic processes, vol. 1875. Springer (1875)
Gilks, W.R.: Full conditional distributions. In: Markov Chain Monte Carlo in Practice, pp. 75–88 (1996)
Breslow, N.E., Clayton, D.G.: Approximate inference in generalized linear mixed models. Journal of the American Statistical Association 88(421), 9–25 (1993)
Bishop, C.M., Nasrabadi, N.M.: Pattern recognition and machine learning, vol. 1. Springer, New York (2006)
Escobar, M., West, M.: Bayesian density estimation and inference using mixtures. Journal of the American Statistical Association 90(430), 577–588 (1995)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The weka data mining software: An update. SIGKDD Explorations 11 (2009)
Gupta, S., Phung, D., Venkatesh, S.: A Bayesian nonparametric joint factor model for learning shared and individual subspaces from multiple data sources. In: Proc. of SIAM Int. Conference on Data Mining (SDM), pp. 200–211 (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Rana, S., Gupta, S.K., Phung, D., Venkatesh, S. (2014). Intervention-Driven Predictive Framework for Modeling Healthcare Data. In: Tseng, V.S., Ho, T.B., Zhou, ZH., Chen, A.L.P., Kao, HY. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2014. Lecture Notes in Computer Science(), vol 8443. Springer, Cham. https://doi.org/10.1007/978-3-319-06608-0_41
Download citation
DOI: https://doi.org/10.1007/978-3-319-06608-0_41
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-06607-3
Online ISBN: 978-3-319-06608-0
eBook Packages: Computer ScienceComputer Science (R0)