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Predicting therapy success of blended cognitive behavioral therapy for depression: application of machine learning methods

  • In the study, predictive models for predicting therapy outcome are created using the dataset from E-COMPARED project, which belongs to the so-called type 3 models that use data from the intervention and preintervention phases to predict treatment outcomes, which can help to adapt intervention to maximize treatment. The predictive models aim to classify patients into two groups, improved and nonimproved. Since it is important to determine whether the models contribute to improvement of treatment, research questions that can contribute to the usage of type 3 models are established. The study focuses on the following three questions: (1) How accurately can the therapy outcome be predicted by various machine learning algorithms? Answering this question can let the people concerned obtain information about the reliability of contemporary predictive models. In addition, if the predictive power of the models is good, it is more likely to be used to assist therapists’ decisions. (2) Which kind of data is more important in predicting the therapy outcome? The answer to this question can show which dataset should be considered first to make better predictive models. Therefore, it can be helpful for researchers who want to make predictive models in the future and eventually help to facilitate personalized therapy. (3) What are the features with strong predictive power? The answer to this question can affect the people concerned, especially therapists. Therapists can use the most influential features revealed to adjust and improve future treatments.

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Metadaten
Verfasserangaben:Yongwoo Kim
URN:urn:nbn:de:gbv:luen4-opus4-13019
URL: https://pub-data.leuphana.de/frontdoor/index/index/docId/1301
Betreuer:Burkhardt Funk (Prof. Dr.), Dieter Riebesehl (Prof. Dr.)
Dokumentart:Masterarbeit
Sprache:Englisch
Datum der Veröffentlichung (online):17.03.2023
Datum der Erstveröffentlichung:24.03.2023
Veröffentlichende Institution:Leuphana Universität Lüneburg, Universitätsbibliothek der Leuphana Universität Lüneburg
Titel verleihende Institution:Leuphana Universität Lüneburg
Datum der Abschlussprüfung:13.09.2018
Datum der Freischaltung:24.03.2023
GND-Schlagwort:Computergestütze Psychotherapie; Verhaltenstherapie; Depression
Bemerkung:
Master Thesis (M.Sc.) im Studiengang: Management & Data Science, [2018]
Fakultät / Forschungszentrum:Fakultät Management und Technologie
Lizenz (Deutsch):License LogoDeutsches Urheberrecht