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

  • Research questions In the study, predictive models for predicting therapy outcome are created using the dataset from E-COMPARED project (see section 4.1), which belongs to type 3 according to Table 1. These 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
Author:Yongwoo Kim
URN:urn:nbn:de:gbv:luen4-opus4-13019
URL: https://pub-data.leuphana.de/frontdoor/index/index/docId/1301
Advisor:Burkhardt Funk (Prof. Dr.), Dieter Riebesehl (Prof. Dr.)
Document Type:Master's Thesis
Language:English
Date of Publication (online):2023/03/17
Date of first Publication:2023/03/24
Publishing Institution:Leuphana Universität Lüneburg, Universitätsbibliothek der Leuphana Universität Lüneburg
Granting Institution:Leuphana Universität Lüneburg
Date of final exam:2018/09/13
Release Date:2023/03/24
GND Keyword:Computergestütze Psychotherapie; Verhaltenstherapie; Depressionen
Note:
Master Thesis (M.Sc.) im Studiengang: Management & Data Science, [2018]
Institutes:Schools / Graduate School
Licence (German):License LogoDeutsches Urheberrecht