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https://hdl.handle.net/20.500.14123/1739
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| Element | Wert |
|---|---|
| Originaltitel | 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 |
| Datenart / Typ | Statistische Auswertungen / Tabellen Kontext- / Begleitmaterialien |
| Ressourcentyp | Datensatz |
| Autor* in / Erzeuger* in | 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) |
| Beschreibung des Datensatzes | 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. |
| Angewandte Methoden | Aggregation Beschreibungen Auswertung und/oder Arbeit mit digitalen Inhalten |
| Schlagwörter | 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 |
| Thematische Einordnung | Data Science |
| Sprache der Ressource | Englisch |
| Anmerkungen zum Datensatz | 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". |
| Verfügbar ab / seit | 2025-01-22T11:31:19Z |
| Datum der Bereitstellung im Katalog | 2025-01-22 |
| Archivierende Einrichtung | Medien- und Informationszentrum (Leuphana Universität Lüneburg 02w2y2t16) |
| Veröffentlicht durch | Medien- und Informationszentrum, Leuphana Universität Lüneburg |
Zugehörige Ressourcen
Übergeordneter Datenbestand: Supplementary Material PhD Kirsten Zantvoort
| Element | Wert |
|---|---|
| Beteiligte Forschende | Zantvoort, Kirsten 0000-0001-9876-054X (Institut für Wirtschaftsinformatik (IIS), Leuphana Universität Lüneburg 02w2y2t16) |
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