Software Development of an Expert System for Mental Health Care and Diagnosis in the Population Victims of the Armed Conflict in Colombia Complying with Indicator 3.4 of SDG 3 Health and Well-Being

This paper describes the psychometric validation procedure for the creation of an Artificial Intelligence (AI) engine based on the theory of precision psychology and expert system-type machine learning algorithms. The focus is on individual coping strategies and the factor of reconciliation in victi...

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Bibliographic Details
Main Authors: De Lange, Anthony Millan, Molinares Brito, Carmen, Dasuki, Karim Hussein, Baena Valencia, Stefany
Format: Conference Proceeding
Language:English
Subjects:
armed conflict
Artificial intelligence
Machine learning algorithms
Mental health
psychological intervention
simulator
Sociology
Software
Statistics
Urban areas
victims
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Summary:This paper describes the psychometric validation procedure for the creation of an Artificial Intelligence (AI) engine based on the theory of precision psychology and expert system-type machine learning algorithms. The focus is on individual coping strategies and the factor of reconciliation in victims of armed conflict. In the case of CONSTANCE IA, the Reconciliation Factor was identified from the Psychosocial Disposition Factors in Conflict questionnaire (CDPC). The study aims to establish whether there is a causal relationship with the variables of the Modified Coping Strategies Scale (EEC-M) questionnaire, identifying psychomarkers that would allow for the creation of a short intervention plan in the areas of prevention, promotion, or rehabilitation for conflict victims relocated in the city of Barranquilla, Colombia. A non-experimental methodology with a cross-sectional design was used, with a total of 363 participants. The results highlight that elements related to economic factors, individual condition, and political concerns in the victims explain the conditions required to generate a reconciliation process in the context of reintegrating into civilian life. One of the conclusions is that the use of artificial intelligence enhances the process of psychological care by providing precision in the etiological information of the illness and the data, which enables discrimination between the health status, symptoms, and disease of the victims in order to contribute to the fulfillment of indicator 3.4 of the SDG: By 2030, reduce by one third premature mortality from non-communicable diseases through prevention and treatment and promote mental health and well-being.
ISSN:2837-4800
DOI:10.1109/IHTC58960.2023.10508840