Using machine learning modeling to identify childhood abuse victims on the basis of personality inventory responses

• Published in: Journal of psychiatric research Vol. 180; pp. 8 - 15
• Main Authors: Sadeghpour, Angelo, Badal, Varsha D., Pogge, David L., O'Donohue, Elizabeth M., Bigdeli, Tim, Harvey, Philip D.
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.12.2024
• Subjects: Artificial intelligence; Childhood abuse; Childhood trauma questionnaire; Machine learning; Minnesota multiphasic personality inventory adolescent version; MMPI-A; Numerical methods; PTSD; Trauma; PTSD; Childhood abuse; Minnesota multiphasic personality inventory adolescent version; Numerical methods; Machine learning; Childhood trauma questionnaire; MMPI-A; Artificial intelligence; Trauma
• ISSN: 0022-3956; 1879-1379; 1879-1379
• DOI: 10.1016/j.jpsychires.2024.09.046

Trauma is very common and associated with significant co-morbidity world-wide, particularly PTSD and frequently other mental health disorders. However, it can be challenging to identify victims of abuse as self-reports can be difficult to elicit due to emotional distress. Better confirmation of a history of significant mistreatment can assist significantly in treatment planning. We evaluate an alternate approach based on machine-learning techniques applied to personality inventory data (Minnesota Personality Inventory, Adolescent Version; MMPI-A) obtained concurrently to examine convergence with reports of past trauma exposure. The Childhood Trauma Questionnaire (CTQ) was administered to 733 child and adolescent inpatients. Statistical and information-theory measures showed that each type of abuse – sexual, physical, and emotional – had a unique “fingerprint” of MMPI-A profiles. In contrast to our previous findings in terms of specific correlations with IQ, individuals positive for Sexual abuse had the fewest MMPI-A elevations, followed by Physical abuse, while those reporting Emotional abuse had the greatest number of elevations. We developed an initial classifier Machine Learning (ML) model for predicting a history of abuse that demonstrates equivalent sensitivity compared to other widely used screening measures. In addition, we show via PCA and cluster analysis that the different levels of severity of emotional abuse present with unique mixtures of personality trait characteristics. Thus, this type of ML mediated analysis could permit at-scale detection of those at potential high risk of a history of abuse by use of real-time information, using a variety of nontransparent data sources.