Personalized treatment selection in routine care: Integrating machine learning and statistical algorithms to recommend cognitive behavioral or psychodynamic therapy.
Objective: This study aims at developing a treatment selection algorithm using a combination of machine learning and statistical inference to recommend patients' optimal treatment based on their pre-treatment characteristics. Methods: A disorder-heterogeneous, naturalistic sample of N = 1,379 outpat...
| Publicado en: | Psychotherapy Research Vol. 31; no. 1; pp. 33 - 52 |
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| Autores principales: | , , , , , |
| Formato: | Artículo |
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Taylor & Francis Ltd
Jan2021
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=147857040&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 147857040 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 10503307 10T jtl: Psychotherapy Research issn: 10503307 maglogo: N pubinfo: dt: Jan2021 vid: 31 iid: 1 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 147857040 10.1080/10503307.2020.1769219 ppf: 33 ppct: 19 formats: tig: atl: Personalized treatment selection in routine care: Integrating machine learning and statistical algorithms to recommend cognitive behavioral or psychodynamic therapy. aug: au: Schwartz, Brian Cohen, Zachary D. Rubel, Julian A. Zimmermann, Dirk Wittmann, Werner W. Lutz, Wolfgang affil: University of Trier, Germany University of California, Los Angeles, CA, United States Justus-Liebig-University Giessen, Germany University of Mannheim, Germany su: Cognitive therapy Patient selection Statistical learning Machine learning Behavior therapists Psychodynamic psychotherapy Random forest algorithms sug: subj: Cognitive therapy Patient selection Statistical learning Machine learning Behavior therapists Psychodynamic psychotherapy Random forest algorithms keyword: machine learning outcome prediction outpatient psychotherapy precision medicine random forest variable selection machine learning outcome prediction outpatient psychotherapy precision medicine random forest variable selection ab: Objective: This study aims at developing a treatment selection algorithm using a combination of machine learning and statistical inference to recommend patients' optimal treatment based on their pre-treatment characteristics. Methods: A disorder-heterogeneous, naturalistic sample of N = 1,379 outpatients treated with either cognitive behavioral therapy or psychodynamic therapy was analyzed. Based on a combination of random forest and linear regression, differential treatment response was modeled in the training data (n = 966) to indicate each individual's optimal treatment. A separate holdout dataset (n = 413) was used to evaluate personalized recommendations. Results: The difference in outcomes between patients treated with their optimal vs. non-optimal treatment was significant in the training data, but non-significant in the holdout data (b = –0.043, p =.280). However, for the 50% of patients with the largest predicted benefit of receiving their optimal treatment, the average percentage of change on the BSI in the holdout data was 52.6% for their optimal and 38.4% for their non-optimal treatment (p =.017; d = 0.33 [0.06, 0.61]). Conclusion: A treatment selection algorithm based on a combination of ML and statistical inference might improve treatment outcome for some, but not all outpatients and could support therapists' clinical decision-making. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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