PubData Leuphana Repository: Dataset size versus homogeneity: A machine learning study on pooling intervention data in e-mental health dropout predictions

Please use this identifier to cite or link to this item: https://doi.org/10.48548/pubdata-1422

Resource type	Journal Article
Title(s)	Dataset size versus homogeneity: A machine learning study on pooling intervention data in e-mental health dropout predictions
DOI	10.48548/pubdata-1422
Handle	20.500.14123/1491
Creator	Zantvoort, Kirsten 0000-0001-9876-054X Hentati Isacsson, Nils 0000-0002-5749-5310 Funk, Burkhardt 0000-0001-5855-2666 Kaldo, Viktor 0000-0002-6443-5279
Abstract	Objective This study proposes a way of increasing dataset sizes for machine learning tasks in Internet-based Cognitive Behavioral Therapy through pooling interventions. To this end, it (1) examines similarities in user behavior and symptom data among online interventions for patients with depression, social anxiety, and panic disorder and (2) explores whether these similarities suffice to allow for pooling the data together, resulting in more training data when prediction intervention dropout. Methods A total of 6418 routine care patients from the Internet Psychiatry in Stockholm are analyzed using (1) clustering and (2) dropout prediction models. For the latter, prediction models trained on each individual intervention's data are compared to those trained on all three interventions pooled into one dataset. To investigate if results vary with dataset size, the prediction is repeated using small and medium dataset sizes. Results The clustering analysis identified three distinct groups that are almost equally spread across interventions and are instead characterized by different activity levels. In eight out of nine settings investigated, pooling the data improves prediction results compared to models trained on a single intervention dataset. It is further confirmed that models trained on small datasets are more likely to overestimate prediction results. Conclusion The study reveals similar patterns of patients with depression, social anxiety, and panic disorder regarding online activity and intervention dropout. As such, this work offers pooling different interventions’ data as a possible approach to counter the problem of small dataset sizes in psychological research.
Language	English
Keywords	Mental Health; Digital Health; Machine Learning
Year of publication in PubData	2024
Publishing type	Parallel publication
Publication version	Published version
Date issued	2024-05-15
Creation context	Research
Notes	This publication was funded by the German Research Foundation (DFG).
Published by	Medien- und Informationszentrum, Leuphana Universität Lüneburg
Related resources	Relations of this publication Relations of this publication