Bayesian network model for diagnosis of Social Anxiety Disorder

Abstract

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 (BNs) are graphical models with significant capabilities that can be used for medical predictions and diagnosis. Social Anxiety Disorder (SAD) 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 SAD diagnosis, in which SAD is analyzed and modeled by Bayesian networks in two phases: construction of BN structure and Conditional Probability Tables (CPTs). This research provides a Bayesian network-based analysis of data, gathered from a number of engineering students.

Causal Modelling for Supporting Planning and Management of Mental Health Services and Systems: A Systematic Review

“…Although causal modelling has been widely applied in general health and mental health care for supporting decision-making (patient level, e.g., for supporting the diagnosis) [35,37,38], it is not the case for the other levels of analysis (micro, meso, and macro). Despite its utility and increased interest in the last years [22,58,61,62], the development of causal modelling is still scarce due to the complexity (the number of variables –sometimes grouped in imprecise domains or constructs- and their causal relationships –sometimes difficult to explain- are very high) and the uncertainty (the statistical nature of the variables are unknown –unreliable or imprecise- and there are missing variables) of real environments.…”

“…In health care, BNs have been applied for decision-making [28,29,30,31] and case assessment: analyzing new diagnosis strategies [32,33,34] and diagnosing social anxiety [35], depression [36,37], and Alzheimer’s disease [38,39]. Despite its reported utility in formalising the explicit knowledge about the structure of a system, in assessing potential responses: XMxji(u) where Xi and Xj are two subsets in the set of variables –endogenous or exogenous (U)- of the BN and Mxj is the action: boldnormaldo(Xj=xj,∀boldnormalj) on it (system) and in evaluating counterfactual sentences (in situation u, X…”