Using the Minnesota Multiphasic Personality Inventory–2 Restructured Form to Predict Functioning After Treatment for Borderline Personality Disorder: A Machine Learning Approach.
Insight into predictors of functioning after treatment for borderline personality disorder (BPD) is limited, despite growing recognition that more focus on other aspects of recovery, especially psychosocial functioning, is warranted. The present study explored the utility of a widely used omnibus as...
| Published in: | Psychological Assessment Vol. 37; no. 6/7; pp. 261 - 273 |
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| Main Authors: | , , , , , |
| Format: | Article |
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American Psychological Association
Jul2025
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| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=185232375&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 185232375 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 10403590 POL jtl: Psychological Assessment issn: 10403590 maglogo: N pubinfo: dt: Jul2025 vid: 37 iid: 6/7 pid: 34 pub: American Psychological Association artinfo: ui: 185232375 10.1037/pas0001385 ppf: 261 ppct: 12 formats: tig: atl: Using the Minnesota Multiphasic Personality Inventory–2 Restructured Form to Predict Functioning After Treatment for Borderline Personality Disorder: A Machine Learning Approach. aug: au: Wibbelink, Carlijn J. M. Sellbom, Martin Grasman, Raoul P. P. P. Arntz, Arnoud Sinnaeve, Roland Kamphuis, Jan H. affil: Department of Clinical Psychology, University of Amsterdam Department of Psychology, University of Otago Department of Psychological Methods, University of Amsterdam Academic Center for Trauma and Personality, Amsterdam, the Netherlands Department of Neurosciences, Mind Body Research, Catholic University Leuven su: Treatment of borderline personality disorder Borderline personality disorder Psychological tests Psychosocial factors Risk assessment Random forest algorithms Treatment effectiveness Descriptive statistics Dialectical behavior therapy Schema therapy Machine learning Psychosocial functioning Predictive validity Evaluation sug: subj: Treatment of borderline personality disorder Borderline personality disorder Psychological tests Psychosocial factors Risk assessment Random forest algorithms Treatment effectiveness Descriptive statistics Dialectical behavior therapy Schema therapy Machine learning Psychosocial functioning Predictive validity Evaluation keyword: borderline personality disorder machine learning Minnesota Multiphasic Personality Inventory–2 Restructured Form predictors treatment response borderline personality disorder machine learning Minnesota Multiphasic Personality Inventory–2 Restructured Form predictors treatment response ab: Insight into predictors of functioning after treatment for borderline personality disorder (BPD) is limited, despite growing recognition that more focus on other aspects of recovery, especially psychosocial functioning, is warranted. The present study explored the utility of a widely used omnibus assessment instrument, the Minnesota Multiphasic Personality Inventory–2 Restructured Form (MMPI-2-RF), to predict change in functioning during treatment for BPD. Data were obtained from a randomized clinical trial into the effectiveness of 2-year evidence-based treatment for BPD (dialectical behavior therapy or schema therapy) among 130 participants diagnosed with BPD. Different machine learning algorithms, including elastic net regression (ENR), random forest, gradient boosting machine, and extreme gradient boosting, were implemented using nested cross-validation. The ENR model had an average explained variance of 42%. A combination of baseline functioning and four MMPI-2-RF scales emerged as key predictors of change in functioning. Baseline functioning was the most important predictor, with lower initial functioning levels related to more improvement. Higher scores on ideas of persecution, somatic complaints, family problems, and disconstraint were associated with less improvement in functioning. Given the risk of overfitting and the lack of an independent data set, future research should focus on the replicability and generalizability of the findings, as well as clarifying the underlying mechanisms. Our study serves as a first step in identifying patients at risk of poor functional outcome after treatment for BPD. Public Significance Statement: Given the increasing emphasis on improving psychosocial functioning alongside symptom recovery in treatment for borderline personality disorder, the present study employed machine learning algorithms to identify predictors of functional outcome after treatment using the Minnesota Multiphasic Personality Inventory–2 Restructured Form. Baseline functioning was the strongest predictor, while ideas of persecution, somatic complaints, family problems, and disconstraint were also associated with change in functioning, tentatively suggesting the importance of the therapeutic relationship. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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