Forecasting New Cases of Bipolar Disorder Using Poisson Hidden Markov Model
International Clinical Neuroscience Journal,
Vol. 5 No. 1 (2018),
15 March 2018
Background: Bipolar disorder (BD) is a major public health problem. In time series count data there may be over dispersion, and serial dependency. In such situation some models that can consider the dependency are needed. The purpose current research was to use Poisson hidden Markov model to forecast new monthly BD instances.
Methods: In current study the dataset including the frequency of new instances of BD from October 2008 to March 2015 in Hamadan Province, the west of Iran were used. We used Poisson hidden Markov with different number of conditions to determine the best model according to Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). Then we used final model to forecast for the next 24 months.
Results: Poisson hidden Markov with two states were chosen as the final model. Each component of dependent mixture model explained one of the states. The results showed that the new BD cases is increase over time and due to forecasting results number of patients for the next 24 months comforted in state two with mean 85.15. The forecast interval was approximately (56, 100).
Conclusion: As the Poisson hidden Markov models was not used to forecast the future states in other prior researches, the findings of this study set forward a forecasting strategy as an alternative to common methods, by considering its deficiencies.
- Bipolar Disorder
- Poisson hidden Markov model
How to Cite
Sadock BJ, Sadock VA. Kaplan and Sadock’s Synopsis of Psychiatry: Behavioral Sciences/Clinical Psychiatry. Lippincott Williams & Wilkins; 2011.
American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders. 4th ed. Washington, DC: American Psychiatric Association; 2000.
Brunello N, Burrows GD, Jonsson B, Judd LL, Kasper S, Keller MB, et al. Critical issues in the treatment of affective disorders. Depression. 1995;3(4):187-98. doi: doi:10.1002/ depr.3050030406.
Angst J, Marneros A. Bipolarity from ancient to modern times: conception, birth and rebirth. J Affect Disord. 2001;67(1-3):3- 19.
Solomon DA, Keitner GI, Miller IW, Shea MT, Keller MB. Course of illness and maintenance treatments for patients with bipolar disorder. J Clin Psychiatry. 1995;56(1):5-13.
Gelenberg AJ, Kane JM, Keller MB, Lavori P, Rosenbaum JF, Cole K, et al. Comparison of standard and low serum levels of lithium for maintenance treatment of bipolar disorder. N Engl J Med. 1989;321(22):1489-93. doi: 10.1056/ nejm198911303212201.
Gitlin MJ, Swendsen J, Heller TL, Hammen C. Relapse and impairment in bipolar disorder. Am J Psychiatry. 1995;152(11):1635-40. doi: 10.1176/ajp.152.11.1635.
Murray CJ, Lopez AD. The global burden of disease and injury series, volume 1: a comprehensive assessment of mortality and disability from diseases, injuries, and risk factors in 1990 and projected to 2020. Geneva : World Health Organization; 1996.
Kennedy N, Everitt B, Boydell J, Van Os J, Jones PB, Murray RM. Incidence and distribution of first-episode mania by age: results from a 35-year study. Psychol Med. 2005;35(6):855- 63.
Laursen TM, Munk-Olsen T, Nordentoft M, Bo Mortensen P. A comparison of selected risk factors for unipolar depressive disorder, bipolar affective disorder, schizoaffective disorder, and schizophrenia from a danish population-based cohort. J Clin Psychiatry. 2007;68(11):1673-81.
Lloyd T, Kennedy N, Fearon P, Kirkbride J, Mallett R, Leff J, et al. Incidence of bipolar affective disorder in three UK cities: results from the AESOP study. Br J Psychiatry. 2005;186:126- 31. doi: 10.1192/bjp.186.2.126.
Zucchini W, MacDonald IL. Hidden Markov models for time series: an introduction using R. CRC Press; 2009.
Ghaffari ME, Ghaleiha A, Taslimi Z, Sarvi F, Amini P, Sadeghifar M, et al. Forecasting Schizophrenia Incidence Frequencies Using Time Series ApproachInt Clin Neurosci J. 2017;4(4):152-6. doi: 10.15171/icnj.2017.06.
Inge A. Hidden Markov Models. Sweden: Stockholm University; 2013.
Lee HC, Tsai SY, Lin HC. Seasonal variations in bipolar disorder admissions and the association with climate: a population-based study. J Affect Disord. 2007;97(1-3):61-9. doi: 10.1016/j.jad.2006.06.026.
Shapira A, Shiloh R, Potchter O, Hermesh H, Popper M, Weizman A. Admission rates of bipolar depressed patients increase during spring/summer and correlate with maximal environmental temperature. Bipolar Disord. 2004;6(1):90-3.
Bonsall MB, Wallace-Hadrill SM, Geddes JR, Goodwin GM, Holmes EA. Nonlinear time-series approaches in characterizing mood stability and mood instability in bipolar disorder. Proc Biol Sci. 2012;279(1730):916-24. doi: 10.1098/ rspb.2011.1246.
Lopez A. Markov models for longitudinal course of youth bipolar disorder. ProQuest; 2008.
DeSantis SM, Bandyopadhyay D. Hidden Markov models for zero-inflated Poisson counts with an application to substance use. Stat Med. 2011;30(14):1678-94. doi: 10.1002/sim.4207.
Hamaker EL, Grasman RP, Kamphuis JH. Regime-switching models to study psychological processes. In: Molenaar PCM, Newell KM. Individual pathways of change: Statistical models for analyzing learning and development. Washington, DC, US: American Psychological Association; 2010:155-168. doi: 10.1037/12140-009.
Cooper B, Lipsitch M. The analysis of hospital infection data using hidden Markov models. Biostatistics. 2004;5(2):223-37. doi: 10.1093/biostatistics/5.2.223.
- Abstract Viewed: 469 times
- PDF Downloaded: 473 times