Original Title	Supplementary Material for the Paper "Dataset size versus homogeneity: A machine learning study on pooling intervention data in e-mental health dropout predictions"
Handle	20.500.14123/1739
Kinds of Data	Statistical Evaluations / Tables Context Materials / Supporting information
Resource Type	Dataset
Creator	Zantvoort, Kirsten  0000-0001-9876-054X (Institut für Wirtschaftsinformatik (IIS), Leuphana Universität Lüneburg  02w2y2t16) Hentati Isacsson, Nils  0000-0002-5749-5310 (Karolinska Institutet  056d84691) Funk, Burkhardt  0000-0001-5855-2666 (Institut für Wirtschaftsinformatik (IIS), Leuphana Universität Lüneburg  02w2y2t16) Kaldo, Viktor  0000-0002-6443-5279 (Karolinska Institutet  056d84691)
Description of the Dataset	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. 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. 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.
Methods	Aggregation Description Analysis of digital content
Keywords	Machinelles Lernen; Data Science; Prognose; Algorithmus; Gesundheitsdaten; Digitale Gesundheit; Mentale Gesundheit; Psychische Störung; Intervention; Therapeutik; Machine Learning; Data Science; Prediction; Algorithm; Health Data; Digital Health; Mental Health; Psychiatric Disorder; Intervention; Therapeutics
Thematic Classification	Data Science
Notes	The supplementary material is available for download. Please visit the article linked below to gain access. You will find the file in the chapter "Supplementary Material".
Published by	Medien- und Informationszentrum, Leuphana Universität Lüneburg
Superordinate Data Collection	× Superordinate Data Collection Close Supplementary Material PhD Kirsten Zantvoort
Related Resources	× Relations of the dataset Close Relations of the dataset