Browse > Article
http://dx.doi.org/10.5626/JOK.2017.44.10.1026

Health State Clustering and Prediction Based on Bayesian HMM  

Sin, Bong-Kee (Pukyong Nat'l Univ.)
Publication Information
Journal of KIISE / v.44, no.10, 2017 , pp. 1026-1033 More about this Journal
Abstract
In this paper a Bayesian modeling and duration-based prediction method is proposed for health clinic time series data using the Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM). HDP-HMM is a Bayesian extension of HMM which can find the optimal number of health states, a number which is highly uncertain and even difficult to estimate under the context of health dynamics. Test results of HDP-HMM using simulated data and real health clinic data have shown interesting modeling behaviors and promising prediction performance over the span of up to five years. The future of health change is uncertain and its prediction is inherently difficult, but experimental results on health clinic data suggests that practical long-term prediction is possible and can be made useful if we present multiple hypotheses given dynamic contexts as defined by HMM states.
Keywords
health clinic data; health state; hierarchical Dirichlet process; hidden Markov model; prediction;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Y.-W. Kim, B.-K. Sin, and H. Choi, "HMM-based health state clustering and Prediction," Proc. 5th Int. Conf. on Ubiq. Computing App. and Wireless Sensor Netowrk (UCAWSN-16), 2016.
2 R. Y. Coley, et al., "A Bayesian hierarchical model for prediction of latent health states from multiple data sources with application to active surveillance of prostrate cancer," [Online]. Available: http://arXive eprint arXiv:1508.07511 [stat.ME], Aug. 2015.
3 L. R. Rabiner, "A tutorial on hidden Markov models and selected applications in speech recognitions," Proc. IEEE, Vol. 77, pp. 257-286, 1989.   DOI
4 M. Beal, Variational algorithms for approximate Bayesian inference, PhD thesis, Univ. London, 2003.
5 P. I. Green, "Reversible jump Markov chain Monte Carlo computation and Bayesian model determination," Biometrika, Vol. 82, pp. 711-732, 1995.   DOI
6 M. Beal, Z. Ghahramani, and C. Rasmussen, "The infinite hidden Markov model," NIPS, 14, 2002.
7 G.H. Juang and L.R. Rabiner "Mixture autoregressive hidden Markov models for speech signals," IEEE Trans. ASSP, Vol. ASSP-33, No. 6, pp. 1404-1413, 1985.
8 M.J. Russell, and R.K. Moore, "Explicit modeling of state occupancy in hidden Markov models for automatic speech recognition," Proc. ICASSP'85 (Tampa, FL), pp. 5-8, 1985.
9 B.-K. Sin, and J.H. Kim, "Nonstationary hidden Markov model," Signal Porcessing, Vol. 46, No. 1, pp. 31-46, Sep. 1995.   DOI
10 B.-K. Sin, "Gamma CDF-based HMM state duration modeling," Journal of KIISE: SW and Appl., Vol. 40, No. 12, pp. 757-763, 2013.
11 Z. Ghahramani, and M. Jordan, "Factorial hidden Markov models," Machine Learning, Vol. 29, pp. 245-273, 1997.   DOI
12 Y.W. Teh, M. Jordan, M. Beal, and D. Blei, "Hierarchical Dirichlet processes," J. Amer .Stat. Assoc., Vol. 101, pp. 1566-1581, 2006.   DOI
13 T. Ferguson, "A Bayesian analysis of some nonparametric problems," Annals of Statistics, Vol. 1, No. 2, pp. 209-230, Mar. 1973.   DOI
14 J. Sethuraman, "A constructive definition of Dirichlet priors," Statistica Sinica, Vol. 4, pp. 639-650, 1994.
15 C.M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006.
16 S. Geman and D. Geman, "Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images," IEEE Tr. PAMI, Vol. 6, pp. 721-741, 1984.
17 H. Robbins, "An empirical Bayes approach to statistics," Proc. 3rd Berkeley Symp. on Math. Stat. and Prob., Vol. 1, pp. 157-163, 1956.
18 National Health Insurance Sharing Service, Korea, [Online]. Available: http://nhiss.nhis.or.kr/op/it/index.do, (Accessed in December, 2016)
19 R.J. Little and B.B. Rubin, Statistical Analysis with Missing Data, John Wiley & Sons, 2014.
20 Y. Zhang, Prediction of financial time series with hidden Markov models, Masters thesis, Simon Fraser University, 2004.
21 M. Stanke, Gene prediction with a hidden Markov model, Doctoral dissertation, Georg-August Universitat, 2003.
22 S.M. Ross, Introduction to Probability Models, (11th ed.), p. 187, 2014.