1. Using machine learning modeling to identify childhood abuse victims on the basis of personality inventory responses.
- Author
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Sadeghpour A, Badal VD, Pogge DL, O'Donohue EM, Bigdeli T, and Harvey PD
- Abstract
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., Competing Interests: Declaration of competing interest Dr. Harvey has received consulting fees or travel reimbursements from Alkermes, Boehringer Ingelheim, Karuna Therapeutics, Merck Pharma, Minerva Neurosciences, SK Pharma, and Sunovion (DSP) Pharma in the past year. He receives royalties from the Brief Assessment of Cognition in Schizophrenia (Owned by WCG Verasci, Inc. and contained in the MCCB). He is chief scientific officer of i-Function, Inc. Dr. Pogge is a full-time employee of Four Winds Hospital. The other authors have no Biomedical conflicts of interest. The current study was based on de-identified archival data from medical records. No personal contact with the participants took place and parents/guardians consented at the time of admission to the use of medical records for research purposes. The University of Miami Institutional Review Board has agreed that these data do not meet criteria for human subjects research requiring informed consent., (Copyright © 2024. Published by Elsevier Ltd.)
- Published
- 2024
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