The Development of a Short Version of the SIMS Using Machine Learning to Detect Feigning in Forensic Assessment
| Autor: | Stefano Ferracuti, Paolo Roma, Graziella Orrù, Merylin Monaro, Cristina Mazza, Giuseppe Sartori |
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| Rok vydání: | 2020 |
| Předmět: |
business.industry
False positives and false negatives Item selection Personal injury medicine.disease Machine learning computer.software_genre Forensic science Psychiatry and Mental health Malingering medicine Artificial intelligence Feature selection Psychic damage SIMS Psychology business Law computer |
| Zdroj: | Psychological Injury and Law. 14:46-57 |
| ISSN: | 1938-9728 1938-971X |
| DOI: | 10.1007/s12207-020-09389-4 |
| Popis: | In the present study, we applied machine learning techniques to evaluate whether the Structured Inventory of Malingered Symptomatology (SIMS) can be reduced in length yet maintain accurate discrimination between consistent participants (i.e., presumed truth tellers) and symptom producers. We applied machine learning item selection techniques on data from Mazza et al. (2019c) to identify the minimum number of original SIMS items that could accurately distinguish between consistent participants, symptom accentuators, and symptom producers in real personal injury cases. Subjects were personal injury claimants who had undergone forensic assessment, which is known to incentivize malingering and symptom accentuation. Item selection yielded short versions of the scale with as few as 8 items (to differentiate between consistent participants and symptom producers) and as many as 10 items (to differentiate between consistent and inconsistent participants). The scales had higher classification accuracy than the original SIMS and did not show the bias that was originally reported between false positives and false negatives. |
| Databáze: | OpenAIRE |
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