Newborns prediction based on a belief Markov chain model

Applied Intelligence

Volumn 43, Issue 3, 2015, Pages 473-486

  Deng, Xinyang ^a,b   Liu, Qi ^b   Deng, Yong ^a  

^a SOUTHWEST UNIVERSITY   (China)

^b VANDERBILT UNIVERSITY SCHOOL OF MEDICINE   (United States)

Author keywords

Belief function; Dempster Shafer evidence theory; Discrete time Markov chain; Newborns prediction; Time series

Indexed keywords

CHAINS; FORECASTING; FORMAL LOGIC; HOSPITALS; PROBABILISTIC LOGICS; QUEUEING NETWORKS; TIME SERIES; UNCERTAINTY ANALYSIS;

BELIEF FUNCTION; DEMPSTER SHAFER EVIDENCE THEORY; DEMPSTER-SHAFER THEORY; DISCRETE TIME MARKOV CHAINS; FUZZY STATE; HOSPITAL MANAGEMENT; MARKOV CHAIN MODELS; PREDICTION-BASED;

MARKOV PROCESSES;

EID: 84941878872     PISSN: 0924669X     EISSN: 15737497     Source Type: Journal    
DOI: 10.1007/s10489-015-0667-9     Document Type: Article

References (76)

1. Aggarwal P, Bhatt D, Devabhaktuni V, Bhattacharya P (2013) Dempster Shafer neural network algorithm for land vehicle navigation application. Inf Sci 253:26–33
2. Allefeld C, Bialonski S (2007) Detecting synchronization clusters in multivariate time series via coarse-graining of Markov chains. Phys Rev E 76(6):066,207
3. Avrachenkov KE, Sanchez E (2002) Fuzzy Markov chains and decision-making. Fuzzy Optim Decis Making 1(2):143–159
4. Bharucha-Reid AT (2012) Elements of the theory of Markov processes and their applications. Courier Dover Publications, New York
5. Blumenthal RM, Getoor RK (2007) Markov processes and potential theory. Courier Dover Publications, New York
6. Buckley JJ, Eslami E (2008) Fuzzy Markov chains: uncertain probabilities. Mathw Soft Comput 9(1):33–41
7. Cecchini M, Lai C, Langher V (2010) Dysphonic newborn cries allow prediction of their perceived meaning. Infant Behav Dev 33(3):314–320
8. Chakraborty K, Mehrotra K, Mohan CK, Ranka S (1992) Forecasting the behavior of multivariate time series using neural networks. Neural Netw 5(6):961–970
9. Costa OLV, Fragoso MD, Marques RP (2006) Discrete-time Markov jump linear systems. Springer, Berlin
10. Cummings DR (2009) Changes in european birth seasonality related to changes in regional sunshine. Biol Rhythm Res 40(2):153–168
11. Cummings DR (2012) Canadian birth seasonality and its possible association with seasonal brightness. Can Stud Popul 39(1–2):45–62
12. De Korvin A, Kleyle R (1998) Expected transition costs based on a Markov model having fuzzy states with an application to policy selection. Stoch Anal Appl 16(1):51–64
13. Dempster AP (1967) Upper and lower probabilities induced by a multivalued mapping. Ann Math Stat 38 (2):325–339
14. Dempster AP (2008) The Dempster-Shafer calculus for statisticians. Int J Approx Reason 48(2):365–377
15. Deng X, Deng Y (2014) On the axiomatic requirement of range to measure uncertainty. Phys A: Stat Mech Appl 406:163–168
16. Deng X, Deng Y, Chan FT (2014a) An improved operator of combination with adapted conflict. Ann Oper Res 223(1):451–459
17. Deng X, Hu Y, Deng Y, Mahadevan S (2014b) Environmental impact assessment based on D numbers. Expert Syst Appl 41(2):635–643
18. Deng X, Hu Y, Deng Y, Mahadevan S (2014c) Supplier selection using AHP methodology extended by D numbers. Expert Syst Appl 41(1):156–167
19. Deng X, Liu Q, Sadiq R, Deng Y (2014d) Impact of roles assignation on heterogeneous populations in evolutionary dictator game. Sci Rep 4:6937
20. Deng X, Wang Z, Liu Q, Deng Y, Mahadevan S (2014e) A belief-based evolutionarily stable strategy. J Theor Biol 361:81–86
21. Deng X, Zheng X, Su X, Chan FT, Hu Y, Sadiq R, Deng Y (2014f) An evidential game theory framework in multi-criteria decision making process. Appl Math Comput 244:783–793
22. Deng X, Hu Y, Chan FT, Mahadevan S, Deng Y (2015) Parameter estimation based on interval-valued belief structures. Eur J Oper Res 241(2):579–582
23. Deng Y (2015) Generalized evidence theory. Applied intelligence. doi:10.1007/s10489-015-0661-2
24. Deng Y, Liu Q, Li YX (2004) Scoring hidden Markov models to discriminate beta-barrel membrane proteins. Comput Biol Chem 28(3):189–194
25. Denoeux T (1995) A k-nearest neighbor classification rule based on Dempster-Shafer theory. IEEE Trans Syst Man Cybern 25(5):804–813
26. Denoeux T (2013) Maximum likelihood estimation from uncertain data in the belief function framework. IEEE Trans Knowl Data Eng 25(1):119–130
27. Galbraith JW (2003) Content horizons for univariate time-series forecasts. Int J Forecast 19(1):43–55
28. Gather U, Imhoff M, Fried R (2002) Graphical models for multivariate time series from intensive care monitoring. Stat Med 21(18):2685–2701
29. Gómez S, Arenas A, Borge-Holthoefer J, Meloni S, Moreno Y (2010) Discrete-time Markov chain approach to contact-based disease spreading in complex networks. Europhys Lett 89(3):38,009
30. Han M, Wang Y (2009) Analysis and modeling of multivariate chaotic time series based on neural network. Expert Syst Appl 36(2):1280–1290
31. Hou Y, Xiong Y, Wang X, Liang X (2014) The effects of a trust mechanism on a dynamic supply chain network. Expert Syst Appl 41(6):3060–3068
32. Kang B, Deng Y, Sadiq R, Mahadevan S (2012) Evidential cognitive maps. Knowl-Based Syst 35:77–86
33. Kleyle R, De Korvin A (1997) Transition probabilities for Markov chains having fuzzy states. Stoch Anal Appl 15(4):527–546
34. Li Y, Lan X, Deng X, Sadiq R, Deng Y (2014) Comprehensive consideration of strategy updating promotes cooperation in the prisoner’s dilemma game. Phys A: Stat Mech Appl 403:284–292
35. Liu HC, You JX, Fan XJ, Lin QL (2014a) Failure mode and effects analysis using D numbers and grey relational projection method. Expert Syst Appl 41(10):4670–4679
36. Liu Q, Zhu YS, Wang BH, Li YX (2003) A HMM-based method to predict the transmembrane regions of beta-barrel membrane proteins. Comput Biol Chem 27(1):69–76
37. Liu ZG, Pan Q, Dezert J (2014b) A belief classification rule for imprecise data. Appl Intell 40(2):214–228
38. Meyn SP, Tweedie RL (2009) Markov chains and stochastic stability. Cambridge University Press, Cambridge
39. Own HS, Abraham A (2012) A new weighted rough set framework based classification for Egyptian NeoNatal Jaundice. Appl Soft Comput 12(3):999–1005
40. Puterman ML (2009) Markov decision processes: discrete stochastic dynamic programming, vol 414. Wiley, New York
41. Rabiner L (1989) A tutorial on hidden markov models and selected applications in speech recognition. Proc IEEE 77(2):257–286
42. Rabiner L, Juang B (1986) An introduction to hidden markov models. IEEE ASSP Mag 3(1):4–16
43. Ragwitz M, Kantz H (2002) Markov models from data by simple nonlinear time series predictors in delay embedding spaces. Phys Rev E 65(5):056,201
44. Razdan A (2009) Transfer matrix method to study extensive air showers. Chaos Solitons Fractals 42(1):253–255
45. Rhazi D, Atalla N (2010) A simple method to account for size effects in the transfer matrix method. J Acoust Soc Am 127(2):30–36
46. Russell D, Douglas A, Allan T (1993) Changing seasonality of birth–a possible environmental effect. J Epidemiol Commun Health 47(5):362–367
47. Salzenstein F, Collet C, Lecam S, Hatt M (2007) Non-stationary fuzzy Markov chain. Pattern Recogn Lett 28(16):2201–2208
48. Schliehe-Diecks S, Kappeler P, Langrock R (2012) On the application of mixed hidden Markov models to multiple behavioural time series. Interface Focus 2(2):180–189
49. Schwartz SL, Gelfand AE, Miranda ML (2010) Joint Bayesian analysis of birthweight and censored gestational age using finite mixture models. Stat Med 29(16):1710–1723
50. Shafer G (1976) A mathematical theory of evidence. Princeton University Press, Princeton
51. Smets P (2005) Decision making in the TBM: the necessity of the pignistic transformation. Int J Approx Reason 38(2):133–147
52. Smets P, Kennes R (1994) The transferable belief model. Artif Intell 66(2):191–234
53. Stewart WJ (1994) Introduction to the numerical solution of Markov Chains. Princeton University Press, Princeton
54. Su X, Mahadevan S, Xu P, Deng Y (2015) Dependence assessment in human reliability analysis using evidence theory and AHP. Risk Analysis -(-): Accept
55. Szolnoki A, Wang Z, Perc M (2012) Wisdom of groups promotes cooperation in evolutionary social dilemmas. Sci Rep 2:0576
56. Teixeira AR, Tomé AM, Bohm M, Puntonet CG, Lang EW (2009) How to apply nonlinear subspace techniques to univariate biomedical time series. IEEE Trans Instrum Meas 58(8):2433–2443
57. Truong KD, Reifsnider OS, Mayorga ME, Spitler H (2013) Estimated number of preterm births and low birth weight children born in the united states due to maternal binge drinking. Matern Child Health J 17(4):677–688
58. Waldhoer T, Klein M, Haidinger G (2002) The effect of the change in air temperature on the number of newborns in vienna 1984–99. Paediatr Perinat Epidemiol 16(2):188–189
59. Wang SJ, Wang Z, Jin T, Boccaletti S (2014a) Emergence of disassortative mixing from pruning nodes in growing scale-free networks. Sci Rep 4:7536
60. Wang Z, Zhu X, Arenzon JJ (2012) Cooperation and age structure in spatial games. Phys Rev E 85 (1):011,149
61. Wang Z, Kokubo S, Tanimoto J, Fukuda E, Shigaki K (2013) Insight into the so-called spatial reciprocity. Phys Rev E 88(4):042,145
62. Wang Z, Szolnoki A, Perc M (2014b) Rewarding evolutionary fitness with links between populations promotes cooperation. J Theor Biol 349:50–56
63. Weiß CH (2011) Rule generation for categorical time series with Markov assumptions. Stat Comput 21(1):1–16
64. Whitmore SK, Zhang X, Taylor AW, Blair JM (2011) Estimated number of infants born to HIV-infected women in the United States and five dependent areas, 2006. J Acquir Immune Defic Syndr 57(3):218–222
65. Wu C, Barnes D (2010) Formulating partner selection criteria for agile supply chains: a Dempster-Shafer belief acceptability optimisation approach. Int J Prod Econ 125(2):284– 293
66. Xu P, Su X, Mahadevan S, Li C, Deng Y (2014) A non-parametric method to determine basic probability assignment for classification problems. Appl Intell 41(3):681–693
67. Yager RR (1992) Decision making under Dempster-Shafer uncertainties. Int J Gen Syst 20(3):233–245
68. Yager RR, Alajlan N (2013) Decision making with ordinal payoffs under Dempster-Shafer type uncertainty. Int J Intell Syst 28(11):1039–1053
69. Yarmohammadi M, Mostafaei H (2012) Markov switching models for time series data with dramatic jumps. Sains Malays 41(3):371–377
70. Yau KKW, Lee AH, Ng ASK (2003) Finite mixture regression model with random effects: application to neonatal hospital length of stay. Comput Stat Data Anal 41(3):359–366
71. Yeon J, Elefteriadou L, Lawphongpanich S (2008) Travel time estimation on a freeway using discrete time Markov chains. Trans Res Part B: Methodol 42(4):325–338
72. Yin GG, Zhang Q (2006) Discrete-time Markov chains: two-time-scale methods and applications, vol 55. Springer, Berlin
73. Yu C, Wong T (2015) An agent-based negotiation model for supplier selection of multiple products with synergy effect. Expert Syst Appl 42(1):223–237
74. Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353
75. Zhang X, Deng Y, Chan FT, Xu P, Mahadevan S, Hu Y (2013) IFSJSP: a novel methodology for the job-shop scheduling problem based on intuitionistic fuzzy sets. Int J Prod Res 51(17):5100–5119
76. Zhao H, Guo S (2014) Selecting green supplier of thermal power equipment by using a hybrid mcdm method for sustainability. Sustainability 6(1):217–235