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dc.contributor.author
Gómez Penedo, Juan Martín
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dc.contributor.author
Schwartz, Brian
dc.contributor.author
Giesemann, Julia
dc.contributor.author
Rubel, Julian A.
dc.contributor.author
Deisenhofer, Anne-Katharina
dc.contributor.author
Lutz, Wolfgang
dc.date.available
2022-08-12T19:40:30Z
dc.date.issued
2021-05
dc.identifier.citation
Gómez Penedo, Juan Martín; Schwartz, Brian; Giesemann, Julia ; Rubel, Julian A.; Deisenhofer, Anne-Katharina; et al.; For whom should psychotherapy focus on problem coping? A machine learning algorithm for treatment personalization; Taylor & Francis; Psychotherapy Research; 32; 2; 5-2021; 151-164
dc.identifier.uri
http://hdl.handle.net/11336/165438
dc.description.abstract
Objective: We aimed to develop and test an algorithm for individual patient predictions of problem coping experiences (PCE) (i.e., patients’ understanding and ability to deal with their problems) effects in cognitive–behavioral therapy. Method: In an outpatient sample with a variety of diagnoses (n=1010), we conducted Dynamic Structural Equation Modelling to estimate within-patient cross-lagged PCE effects on outcome during the first ten sessions. In a randomly selected training sample (2/3 of the cases), we tried different machine learning algorithms (i.e., ridge regression, LASSO, elastic net, and random forest) to predict PCE effects (i.e., the degree to which PCE was a time-lagged predictor of symptoms), using baseline demographic, diagnostic, and clinically-relevant patient features. Then, we validated the best algorithm on a test sample (1/3 of the cases). Results: The random forest algorithm performed best, explaining 14.7% of PCE effects variance in the training set. The results remained stable in the test set, explaining 15.4% of PCE effects variance. Conclusions: The results show the suitability to perform individual predictions of process effects, based on patients’ initial information. If the results are replicated, the algorithm might have the potential to be implemented in clinical practice by integrating it into monitoring and therapist feedback systems.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Taylor & Francis
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dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
BASELINE PATIENT CHARACTERISTICS
dc.subject
COGNITIVE-BEHAVIORAL THERAPY (CBT)
dc.subject
INDIVIDUAL PREDICTIONS
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MACHINE LEARNING
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PROBLEM COPING EXPERIENCES
dc.subject.classification
Otras Psicología
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dc.subject.classification
Psicología
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dc.subject.classification
CIENCIAS SOCIALES
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dc.title
For whom should psychotherapy focus on problem coping? A machine learning algorithm for treatment personalization
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2022-08-12T10:04:58Z
dc.identifier.eissn
1468-4381
dc.journal.volume
32
dc.journal.number
2
dc.journal.pagination
151-164
dc.journal.pais
Estados Unidos
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dc.description.fil
Fil: Gómez Penedo, Juan Martín. University Of Trier; Alemania. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.description.fil
Fil: Schwartz, Brian. University Of Trier; Alemania
dc.description.fil
Fil: Giesemann, Julia. University Of Trier; Alemania
dc.description.fil
Fil: Rubel, Julian A.. Justus Liebig University Giessen; Alemania
dc.description.fil
Fil: Deisenhofer, Anne-Katharina. University Of Trier; Alemania
dc.description.fil
Fil: Lutz, Wolfgang. University Of Trier; Alemania
dc.journal.title
Psychotherapy Research
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1080/10503307.2021.1930242
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.tandfonline.com/doi/full/10.1080/10503307.2021.1930242
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