Risk assessment of human neural tube defects using a Bayesian belief network

Stochastic Environmental Research and Risk Assessment

Volumn 24, Issue 1, 2010, Pages 93-100

  Liao, Yilan ^a   Wang, Jinfeng ^a   Guo, Yaoqin ^a   Zheng, Xiaoying ^b  

^a INSTITUTE OF GEOLOGY AND GEOPHYSICS   (China)

^b PEKING UNIVERSITY   (China)

Author keywords

Bayesian belief network; Case effect relationship; Data discretization; Neural tube birth defects

Indexed keywords

BAYESIAN BELIEF NETWORKS; BIRTH DEFECTS; CONFIDENCE LEVELS; ENVIRONMENTAL FACTORS; EXISTING METHOD; GEOGRAPHICAL DISTRIBUTION; NEURAL TUBE DEFECTS; REGION OF INTEREST; RELATIONSHIP DATA; SPATIAL CORRELATIONS;

DEFECTS; NEURAL NETWORKS; RISK ASSESSMENT; RISK MANAGEMENT; RURAL AREAS; TUBES (COMPONENTS);

BAYESIAN NETWORKS;

BAYESIAN ANALYSIS; ENVIRONMENTAL FACTOR; RISK ASSESSMENT; RURAL AREA;

CHINA;

EID: 77949773311     PISSN: 14363240     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00477-009-0303-5     Document Type: Article

References (31)

1. Aronsky D, Haug PJ (2000) Automatic identification of patients eligible for a pneumonia guideline. Proc/AMIA Annu Symp 12-16.
2. Burnside E, Rubin D, Shachter R (2000) A Bayesian network for mammography. Proc/AMIA Annu Symp 106-110.
3. Canales RA, Leckie JO (2007) Application of a stochastic model to estimate children's short-term residential exposure to lead. Stoch Environ Res Risk Assess 21: 737-745.
4. Carmichael SL, Nelson V, Shaw GM, Wasserman CR, Croen LA (2003) Socio-economic status and risk of conotruncal heart defects and orofacial clefts. Paediatr Perinat Epidemiol 17: 264-271.
5. Carmona RH (2005) The global challenges of birth defects and disabilities. Lancet 366: 1142-1144.
6. Chi WX, Wang JF, Li XH, Zheng XY, Liao YL (2007) Application of GIS-based spatial filtering method for neural tube defects disease mapping. J Wuhan Univ (Nat Sci Ed) 12(6): 1125-1130.
7. Christakos G (2002) On the assimilation of uncertain physical knowledge bases: Bayesian and non-Bayesian techniques. Adv Water Resour 25: 1257-1274.
8. Frey L, Hauser WA (2003) Epidemiology of neural tube defects. Epilepsia 44: 4-13.
9. Hamilton PW, Montironi R, Abmayr W, Bibbo M, Anderson N, Thompson D, Bartels PH (1995) Clinical applications of Bayesian belief networks in pathology. Pathologica 87(3): 237-245.
10. Heckerman D (1997) Bayesian networks for data mining. Data Min Knowl Disc 1(1): 79-119.
11. Heern GA, Tyler J, Mandeya A (2003) Agricultural chemical exposures and birth defects in the Eastern Cape Province, South Africa A case-control study. Environ Health 2(1): 11-18.
12. Hemmi I (2008) Bayesian estimation of the incidence rate in birth defects monitoring. Congenit Anom 28(2): 103-109.
13. Hu HT (2003) Methods to prevent Shanxi birth defect. China. org. cn. Available viable by http://www.china.org.cn/english/2003/sep/74927.htm.
14. Ismaila AS, Canty A, Thabane L (2007) Comparison of Bayesian and frequentist approaches in modeling risk of preterm birth near the Sydney Tar Ponds, Nova Scotia, Canada. BMC Med Res Methodol 7: 39-52.
15. Lammer EJ (1998) Gene-environment analyses in human birth defects research. Neurotoxicol Teratol 25(3): 351.
16. Lee SM, Abbott PA (2003) Bayesian networks for knowledge discovery in large databasets: basics for nurse researchers. J Biomed Inform 36: 389-399.
17. Lee E, Park Y, Shin JG (2008) Large engineering project risk management using a Bayesian belief network. Expert Syst Appl doi: 10. 1016/j. eswa. 2008. 07. 057.
18. Li XH, Wang JF, Liao YL, Meng B, Zheng XY (2006) A geo-analysis for the environmental cause of human birth defects. Toxicol Environ Chem 88(3): 551-559.
19. Lin JH, Haug PJ (2008) Exploiting missing clinical data in Bayesian network modeling for predicting medical problems. J Biomed Inform 41: 1-14.
20. Maglogiannis I, Zafiropoulos E, Platis A, Lambrinoudakis C (2006) Risk analysis of a patient monitoring system using Bayesian network modeling. J Biomed Inform 39: 637-647.
21. Maskery SM, Hu H, Hooke J, Shriver CD, Liebman MN (2008) A Bayesian derived network of breast pathology co-occurrence. J Biomed Inform 41: 242-250.
22. McCabe B, AbouRizk SM, Goebel R (1998) Belief networks for construction performance diagnostics. J Comput Civil Eng ASCE 12(2): 93-100.
23. Ritz B, Yu F, Fruin S, Chapa G, Shaw GM, Harris JA (2002) Ambient air pollution and risk of birth defects in southern California. Am J Epidemiol 155(1): 17-25.
24. Rushton G, Lolonis P (1996) Exploratory spatial analysis of birth defect rates in an urban population. Stat Med 15: 717-726.
25. Sankoh OA, Berke O, Simboro S, Becher H (2002) Bayesian and GIS mapping of childhood mortality in rural Burkina Faso. Control of Tropical Infectious Diseases, Uni-Heidelberg Discussion Paper.
26. Sebastiani P, Nolan VG, Baldwin CT, Abad-Grau MM, Wang L, Adewoye AH, McMahon LC, Farrer LA, Taylor JG, Kato GJ, Gladwin MT, Steinberg MH (2008) A network model to predict the risk of death in sickle cell disease. Blood 41(3): 432-441.
27. UIman C, Taneli F, Oksel F, Hakerlerler H (2005) Zinc-deficient sprouting blight potatoes and their possible relation with neural tube defects. Cell Biochem Funct 23: 69-72.
28. Uusitalo L (2007) Advantages and challenges of Bayesian networks in environmental modeling. Ecol Model 203: 312-318.
29. Wang JF, Li XH, Christakos G, Liao YL, Zhang T, Gu X, Zheng XY (2008) Geographical detectors-based health risk assessment and its application in the neural tube defects study of the Heshun Region, China. Int J Geogr Inf Sci (in press).
30. Wiwanitkit V (2008) Estimating cancer risk due to benzene exposure in some urban areas in Bangkok. Stoch Environ Res Risk Assess 22: 135-137.
31. Wu JL, Wang JF, Meng B, Chen G, Pang LH, Song XM, Zhang KL, Zhang T, Zheng XY (2004) Exploratory spatial data analysis for the identification of risk factors to birth defects. BMC Public Health 4: 23-33.