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

Neural tube defects (NTDs) constitute the most common type of birth defects. How much risk of NTDs could an area take? The answer to this question will help people understand the geographical distribution of NTDs and explore its environmental causes. Most existing methods usually take the spatial correlation of cases into account and rarely consider the effect of environmental factors. However, especially in rural areas, the NTDs cases have a little effect on each other across space, whereas the role of environmental factors is significant. To demonstrate these points, Heshun, a county with the highest rate of NTDs in China, was selected as the region of interest in the study. Bayesian belief network was used to quantify the probability of NTDs occurred at villages with no births. The study indicated that the proposed method was easy to apply and high accuracy was achieved at a 95% confidence level.

[1]  I. Hemmi.  Bayesian Estimation of the Incidence Rate in Birth Defects Monitoring , 1987 .

[2]  P H Bartels,et al.  Clinical applications of Bayesian belief networks in pathology. , 1995, Pathologica.

[3]  G. Rushton,et al.  Exploratory spatial analysis of birth defect rates in an urban population. , 1996, Statistics in medicine.

[4]  Simaan M. AbouRizk,et al.  BELIEF NETWORKS FOR CONSTRUCTION PERFORMANCE DIAGNOSTICS , 1998 .

[5]  Ross D. Shachter,et al.  A Bayesian network for mammography , 2000, AMIA.

[6]  Peter J. Haug,et al.  Automatic identification of patients eligible for a pneumonia guideline , 2000, AMIA.

[7]  H. Becher,et al.  Bayesian and GIS Mapping of Childhood Mortality in Rural Burkina Faso , 2002 .

[8]  G. Shaw,et al.  Ambient air pollution and risk of birth defects in Southern California. , 2002, American journal of epidemiology.

[9]  George Christakos,et al.  On the assimilation of uncertain physical knowledge bases: Bayesian and non-Bayesian techniques. , 2002 .

[10]  A. Mandeya,et al.  Agricultural chemical exposures and birth defects in the Eastern Cape Province, South Africa A case – control study , 2003, Environmental health : a global access science source.

[11]  G. Shaw,et al.  Socio-economic status and risk of conotruncal heart defects and orofacial clefts. , 2003, Paediatric and perinatal epidemiology.

[12]  Lauren C. Frey,et al.  Epidemiology of Neural Tube Defects , 2003, Epilepsia.

[13]  Sun-Mi Lee,et al.  Bayesian networks for knowledge discovery in large datasets: basics for nurse researchers , 2003, J. Biomed. Informatics.

[14]  Xiaoying Zheng,et al.  Exploratory spatial data analysis for the identification of risk factors to birth defects , 2004, BMC public health.

[15]  David Heckerman,et al.  Bayesian Networks for Data Mining , 2004, Data Mining and Knowledge Discovery.

[16]  C. Ulman,et al.  Zinc‐deficient sprouting blight potatoes and their possible relation with neural tube defects , 2005, Cell biochemistry and function.

[17]  R. Carmona,et al.  The global challenges of birth defects and disabilities , 2005, The Lancet.

[18]  Xiaoying Zheng,et al.  A geological analysis for the environmental cause of human birth defects based on GIS , 2006 .

[19]  Costas Lambrinoudakis,et al.  Risk analysis of a patient monitoring system using Bayesian Network modeling , 2006, J. Biomed. Informatics.

[20]  María M. Abad-Grau,et al.  A network model to predict the risk of death in sickle cell disease. , 2007, Blood.

[21]  Laura Uusitalo,et al.  Advantages and challenges of Bayesian networks in environmental modelling , 2007 .

[22]  Wang Jinfeng,et al.  Application of GIS-based spatial filtering method for neural tube defects disease mapping , 2007 .

[23]  Y. Liao,et al.  Application of GIS-based spatial filtering method for neural tube defects disease mapping , 2007, Wuhan University Journal of Natural Sciences.

[24]  Angelo J. Canty,et al.  Comparison of Bayesian and frequentist approaches in modelling risk of preterm birth near the Sydney Tar Ponds, Nova Scotia, Canada , 2007, BMC medical research methodology.

[25]  James O. Leckie,et al.  Application of a stochastic model to estimate children’s short-term residential exposure to lead , 2007 .

[26]  V. Wiwanitkit.  Estimating cancer risk due to benzene exposure in some urban areas in Bangkok , 2008 .

[27]  Peter J. Haug,et al.  Exploiting missing clinical data in Bayesian network modeling for predicting medical problems , 2008, J. Biomed. Informatics.

[28]  Hai Hu,et al.  A Bayesian derived network of breast pathology co-occurrence , 2008, J. Biomed. Informatics.

[29]  Yongtae Park,et al.  Large engineering project risk management using a Bayesian belief network , 2009, Expert Syst. Appl..

[30]  Xiaoying Zheng,et al.  Geographical Detectors‐Based Health Risk Assessment and its Application in the Neural Tube Defects Study of the Heshun Region, China , 2010, Int. J. Geogr. Inf. Sci..