EP142 Diagnosis of pain deception using MMPI-2 based on XGBoost machine learning algorithm: a single-blinded randomized controlled trial

Background and AimsAssessing pain deception is challenging due to its subjective nature. This study explores using Minnesota Multiphasic Personality Inventory-2 (MMPI-2) analysis with machine learning (ML) to detect malingering. We hypothesize that ML analysis of MMPI-2 can detect pain deception. Th...

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Veröffentlicht in:Regional anesthesia and pain medicine Jg. 48; H. Suppl 1; S. A118
Hauptverfasser: Moon, Ho Sik, Kim, Sung-Jun
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Secaucus BMJ Publishing Group Ltd 01.09.2023
BMJ Publishing Group LTD
Schlagworte:
Accuracy
Clinical trials
Deception
ePoster session 4 – Station 6
Machine learning
malingering
Medical diagnosis
MMPI-2
Pain
pain deception
XGBoost
ISSN:1098-7339, 1532-8651
Online-Zugang:Volltext
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Zusammenfassung:Background and AimsAssessing pain deception is challenging due to its subjective nature. This study explores using Minnesota Multiphasic Personality Inventory-2 (MMPI-2) analysis with machine learning (ML) to detect malingering. We hypothesize that ML analysis of MMPI-2 can detect pain deception. The main goal of this study was to evaluate the diagnostic value for pain deception using ML analysis with MMPI-2 scales, considering accuracy, precision, recall, and f1-score as diagnostic parameters.MethodsWe conducted a single-blinded, randomized controlled trial to evaluate the diagnostic value of the MMPI-2, Waddell’s sign, and salivary alpha amylase (SAA). We grouped the non-deception (ND) group and the deception (D) group randomly.ResultsOf the total of 96 participants, 46 were assigned to group D and 50 to group ND. In the logistic regression analysis, pain and MMPI-2 did not show diagnostic value, however in ML analysis, values of selected MMPI-2 (sMMPI-2) which is related to malingering showed accuracy 0.684, precision 0.667, recall 0.800, and f1-score came out as 0.727. When performed with whole MMPI-2(wMMPI-2), accuracy 0.621, precision 0.692, recall 0.562, and f1-score 0.651 was showed. The f1-score was higher in sMMPI-2.ConclusionsWe suggest that the diagnosis of pain deception through the pattern changes of MMPI-2 scales using ML could be valuable. It could be a benefit to clinicians to detect deception exactly and objectively in various situations. Further large-scale studies would be needed to screen and predict more preciselyAbstract EP142 Table 1Descriptive statistics according to the groupAbstract EP142 Table 2Diagnostic value of exaggeration scales and somatic inconvenience scales of MMPI-2, and Waddell’s signAbstract EP142 Table 3XGBoost analysis of MMPI-2 scales to classify the D and ND groupInstitutional Review Board
Bibliographie:ESRA Abstracts, 40th Annual ESRA Congress, 6–9 September 2023
ObjectType-Conference Proceeding-1
SourceType-Scholarly Journals-1
content type line 14
ISSN:1098-7339
1532-8651
DOI:10.1136/rapm-2023-ESRA.207