Bayesian network modeling for diagnosis of social anxiety using some cognitive-behavioral factors

被引：13

作者：

Shojaei Estabragh Z. ^{[1
]}

Riahi Kashani M.M. ^{[1
]}

Jeddi Moghaddam F. ^{[1
]}

Sari S. ^{[1
]}

Taherifar Z. ^{[2
]}

Moradi Moosavy S. ^{[1
]}

Sadeghi Oskooyee K. ^{[1
]}

机构：

[1] Department of Computer Engineering, Islamic Azad University, North Tehran Branch, Tehran

[2] Department of Clinical Psychology, Shahid Beheshti University, Tehran

来源：

Network Modeling Analysis in Health Informatics and Bioinformatics | 2013年 / 2卷 / 4期

关键词：

Bayesian networks; Cognitive-behavioral factors; Decision making; Disease diagnosis; Social anxiety;

D O I：

10.1007/s13721-013-0042-x

中图分类号：

学科分类号：

摘要：

Nowadays a lot of systems are developed to predict or suggest a diagnosis about the health level of a patient for helping physicians in their decisional process. Recent researches prove that decisional systems implemented by Bayesian networks represent an efficient tool for medical healthcare practitioners. Bayesian networks are graphical models with significant capabilities that can be used for medical predictions and diagnosis. Social anxiety disorder is the third most common psychiatric disorder in America behind depression and alcohol abuse. This paper focuses on the use of Bayesian network in assisting social anxiety disorder diagnosis. The network is constructed manually based on the domain knowledge and the conditional probability tables are learned by using the Netica software. This research provides a Bayesian network-based analysis of data set, collected from a number of university students. The model can be an efficient tool for medical healthcare practitioners in diagnosis of social anxiety. © 2013 Springer-Verlag Wien.

引用

页码：257 / 265

页数：8

共 46 条

[1]

Diagnostic and Statistical Manual of Mental Disorders, (1994)

[2]

Bandura A., Social Learning Theory, (1977)

[3]

Berman R.M., Schneier F.R., Symptomatology and diagnosis of social anxiety disorder, Social Anxiety Disorder, pp. 1-18, (2004)

[4]

Brunello N., den Boe J.A., Judd L.L., Kasper S., Kelsey J.E., Lader M., Lecrubier Y., Lepine J.P., Lydiard R.B., Mendlewicz J., Montgomery S.A., Racagni G., Stein M.B., Wittchen H.U., Social phobia: diagnosis and epidemiology, neurobiology and pharmacology, comorbidity and treatment, J Affect Disord, 60, 1, pp. 61-74, (2000)

[5]

Castro A.K.A., Pinheiro P.R., Pinheiro M.C.D., An approach for the neuropsychological diagnosis of Alzheimer's disease: a hybrid model in decision making, RSKT 2009, Lncs, 5589, pp. 216-223, (2009)

[6]

Clark D.M., Wells A., A cognitive model of social phobia, Social Phobia: Diagnosis, Assessment, and Treatment, pp. 69-93, (1995)

[7]

Collins N.L., Read S.J., Adult attachment, working models and relationship quality in dating couples, J Pers Soc Psychol, 58, pp. 644-663, (1990)

[8]

Connor K.M., Davidson J.R.T., Churchill L.E., Sherwood A., Foa E., Wesler R.H., Psychometric properties of the Social Phobia Inventory (SPIN), Br J Psychiatry, 176, pp. 379-386, (2000)

[9]

Coplan R.J., Wilson J., Frohlick S.L., Zelenski J.M., A person-oriented analysis of behavioral inhibition and behavioral activiation in childhood, Personality Individ Differ, 41, pp. 917-927, (2006)

[10]

Curiac D.I., Vasile G., Banias O., Volosencu C., Albu A., Bayesian network model for diagnosis of psychiatric diseases, Proceedings of the ITI 2009 31st international conference on information technology interfaces, pp. 61-66, (2009)

← 1 2 3 4 5 →