Estimation of minimal data sets sizes for machine learning predictions in digital mental health interventions
Kirsten Zantvoort; Barbara Nacke; Dennis Görlich; Silvan Hornstein; Corinna Jacobi; Burkhardt Funk · 2024 · npj Digital Medicine
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Abstract
Artificial intelligence promises to revolutionize mental health care, but small dataset sizes and lack of robust methods raise concerns about result generalizability. To provide insights on minimal necessary data set sizes, we explore domain-specific learning curves for digital intervention dropout predictions based on 3654 users from a single study (ISRCTN13716228, 26/02/2016). Prediction performance is analyzed based on dataset size (N = 100-3654), feature groups (F = 2-129), and algorithm choice (from Naive Bayes to Neural Networks). The results substantiate the concern that small datasets
Abstract by Kirsten Zantvoort; Barbara Nacke; Dennis Görlich; Silvan Hornstein; Corinna Jacobi; Burkhardt Funk, npj Digital Medicine (2024) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.1038/s41746-024-01360-w