Predicting maintenance lithium response for bipolar disorder from electronic health records—a retrospective study
Even if we failed to predict successful monotherapy olanzapine treatment, and so to definitively separate lithium <i>vs</i>. olanzapine responders, the characterization of the two groups may be used for classification by proxy. This can, in turn, be useful for establishing maintenance therapy. The further exploration of machine learning methods on EHR data for drug treatment selection could in the future play a role for clinical decision support. Signals in the data encourage further experiments with larger datasets to definitively separate lithium <i>vs</i>. olanzapine responders.
Authors
- Fehmi Ben Abdesslem (ORCID: https://orcid.org/0000-0001-7866-143X)
- Magnus Boman (ORCID: https://orcid.org/0000-0001-7949-1815)
- David Osborn (ORCID: https://orcid.org/0000-0003-2519-1539)
- Sandra Eloranta (ORCID: https://orcid.org/0000-0001-5806-0573)
- Joseph Hayes (ORCID: https://orcid.org/0000-0003-2286-3862)
Institutions
- University of London (GB)
- Karolinska University Hospital (SE)
- RISE Research Institutes of Sweden (SE)
- Karolinska Institutet (SE)
- University College London (GB)
- Camden and Islington NHS Foundation Trust (GB)
Publication Details
- Journal
- PeerJ
- Published
- 2024-10-14
- DOI
- https://doi.org/10.7717/peerj.17841
- Citations
- 4
- Primary Topic
- Bipolar Disorder and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 1.54
Funders
- Wellcome Trust
- UK Research and Innovation
- National Institute for Health and Care Research
- University College London
- Medical Research Council