Medical ontology learning framework to investigate daytime impairment in insomnia disorder and treatment effects
The study demonstrates that DiSMOL offers a more accurate method for identifying specific disease aspects, such as daytime impairment in insomnia, than traditional coding systems. These findings highlight the potential of specialized ontologies to enhance the representation and analysis of real-world clinical data, with important implications for healthcare policy and personalized medicine.
Authors
- Alexander Büsser
- Michael A. Grandner (ORCID: https://orcid.org/0000-0002-4626-754X)
- W. Vaughn McCall (ORCID: https://orcid.org/0000-0001-5035-7858)
- Moritz Freidank
- Antonio Olivieri (ORCID: https://orcid.org/0000-0002-9198-6834)
- Renato Durrer
- Matteo Togninalli
Institutions
- University of Arizona (US)
- Augusta University (US)
- Med-Consilium (Switzerland) (CH)
- Idorsia (Switzerland) (CH)
Publication Details
- Journal
- Communications Medicine
- Published
- 2025-02-28
- DOI
- https://doi.org/10.1038/s43856-024-00698-2
- Citations
- 2
- Primary Topic
- Mental Health Research Topics
- Type
- article
- Field-Weighted Citation Impact
- 4.24