Predicting Remission in Schizophrenia Using Machine Learning—Assessing the Impact of Sample Size and Predictor Overinclusion
Supervised learning models can generate better-than-chance predictions in schizophrenia from small datasets, but this requires that not too many uninformative predictors are included. Since highly predictive models have not yet been established for schizophrenia-and since strong linear predictors are easy to identify-commonly collected clinical trial data likely do not contain predictors with strong linear relations to clinically relevant outcomes. If correct, future machine learning analyses should focus on maximizing the probability of identifying weakly predictive features.
Authors
- Magnus Hieronymus (ORCID: https://orcid.org/0000-0002-0786-7438)
- Søren Dinesen Østergaard (ORCID: https://orcid.org/0000-0002-8032-6208)
- Staffan Nilsson (ORCID: https://orcid.org/0000-0003-4748-0446)
- Jakob Näslund (ORCID: https://orcid.org/0000-0003-2782-5160)
- Fredrik Hieronymus (ORCID: https://orcid.org/0000-0003-0930-6068)
- Alexander Lisinski (ORCID: https://orcid.org/0000-0003-3130-0172)
- Axel Sjöstedt
Institutions
- Swedish Meteorological and Hydrological Institute (SE)
- Aarhus University (DK)
- Aarhus University Hospital (DK)
- University of Gothenburg (SE)
Publication Details
- Journal
- Acta Psychiatrica Scandinavica
- Published
- 2025-09-10
- DOI
- https://doi.org/10.1111/acps.70037
- Citations
- 1
- Primary Topic
- Schizophrenia research and treatment
- Type
- article
- Field-Weighted Citation Impact
- 1.47
Funders
- Lundbeckfonden
- Vetenskapsrådet
- H. Lundbeck A/S
- Janssen Research and Development