Machine learning-enabled detection of attention-deficit/hyperactivity disorder with multimodal physiological data: a case-control study
This study underscores the potential of physiological signals as valuable diagnostic indicators of adult ADHD. For the first time, to the best of our knowledge, our findings demonstrate that multimodal physiological data collected via wearable devices can complement traditional diagnostic approaches. Further research is warranted to explore the clinical applications and long-term implications of utilizing physiological markers in ADHD diagnosis and management.
Authors
- Artemios Pehlivanidis (ORCID: https://orcid.org/0000-0002-3328-6258)
- Charalabos Papageorgiou (ORCID: https://orcid.org/0000-0001-7635-1956)
- Charalampos Tsirmpas (ORCID: https://orcid.org/0009-0000-9110-7716)
- Dimitrios Andrikopoulos (ORCID: https://orcid.org/0000-0001-7141-7661)
- Georgia Vassiliou
- Panagiotis Fatouros (ORCID: https://orcid.org/0000-0003-1685-0600)
Institutions
- National and Kapodistrian University of Athens (GR)
- Rapt Therapeutics (United States) (US)
- University Mental Health Research Institute (GR)
- Eginition Hospital (GR)
Publication Details
- Journal
- BMC Psychiatry
- Published
- 2024-08-05
- DOI
- https://doi.org/10.1186/s12888-024-05987-7
- Citations
- 19
- Primary Topic
- Attention Deficit Hyperactivity Disorder
- Type
- article
- Field-Weighted Citation Impact
- 7.33