Probabilistic graphical model for the evaluation of the emotional and dramatic personality disorders

Abstract

Personality disorders are psychological ailments with a major negative impact on patients, their families, and society in general, especially those of the dramatic and emotional type. Despite all the research, there is still no consensus on the best way to assess and treat them. Traditional assessment of personality disorders has focused on a limited number of psychological constructs or behaviors using structured interviews and questionnaires, without an integrated and holistic approach. We present a novel methodology for the study and assessment of personality disorders consisting in the development of a Bayesian network, whose parameters have been obtained by the Delphi method of consensus from a group of experts in the diagnosis and treatment of personality disorders. The result is a probabilistic graphical model that represents the psychological variables related to the personality disorders along with their relations and conditional probabilities, which allow identifying the symptoms with the highest diagnostic potential. This model can be used, among other applications, as a decision support system for the assessment and treatment of personality disorders of the dramatic or emotional cluster. In this paper, we discuss the need to validate this model in the clinical population along with its strengths and limitations.

Unveiling Fall Triggers in Older Adults: A Machine Learning Graphical Model Analysis

“…The application of graphical models reflects a growing trend in social sciences [57,58], and various health disciplines [14,15,[59][60][61]. Particularly, Bhushan et al utilized the Gaussian graphical model to explore and visualize the relationships between items and factors in environmental psychology research [57], and Kalisch et al applied graphical models on the International Classification of Functioning, Disability, and Health data to visualize the dependence structure of the data set, dimension reduction, and comparison of subpopulations in studying human functioning [60].…”