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https://doi.org/10.48548/pubdata-1422
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| Element | Wert |
|---|---|
| Ressourcentyp | Zeitschriftenartikel |
| Titel | 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 |
| Autor*in | Zantvoort, Kirsten  0000-0001-9876-054XHentati Isacsson, Nils 0000-0002-5749-5310Funk, Burkhardt 0000-0001-5855-2666Kaldo, 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. |
| Sprache | Englisch |
| Schlagwörter | Mental Health; Digital Health; Machine Learning |
| Jahr der Veröffentlichung in PubData | 2024 |
| Art der Veröffentlichung | Zweitveröffentlichung |
| Publikationsversion | Veröffentlichte Version |
| Datum der Erstveröffentlichung | 2024-05-15 |
| Entstehungskontext | Forschung |
| Fakultät / Abteilung | Fakultät Management und Technologie |
| Anmerkungen | This publication was funded by the German Research Foundation (DFG). |
| Verfügbar ab / seit | 2024-11-06T14:59:34Z |
| Archivierende Einrichtung | Medien- und Informationszentrum (Leuphana Universität Lüneburg  02w2y2t16) |
| Veröffentlicht durch | Medien- und Informationszentrum, Leuphana Universität Lüneburg |
Informationen zur Erstveröffentlichung
| Element | Wert |
|---|---|
| Ressourcentyp | Zeitschrift |
| Titel des Ressourcentyps | Digital Health |
| Identifier | DOI: 10.1177/20552076241248920 |
| Publikationsjahr | 2024 |
| Band | 10 |
| Verlag / Anbieter | SAGE |
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| Datei | Beschreibung | Größe | Format | |
|---|---|---|---|---|
Zantvoort_dataset_size_versus_homogeneity.pdf Lizenz: 
open-access | 817.65 kB | Adobe PDF | Öffnen/Anzeigen |
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