Clinical validation and machine learning optimization of MyCog: A self‐administered cognitive screener for primary care settings
Findings suggest MyCog accurately detects cognitive impairment via a streamlined self-administered app that efficiently fits into primary care workflows.
Authors
- Stephanie Batio
- Michael S. Wolf (ORCID: https://orcid.org/0000-0002-4342-6517)
- Julia Yoshino Benavente (ORCID: https://orcid.org/0000-0003-0549-1809)
- Katherina Hauner (ORCID: https://orcid.org/0009-0008-5648-4979)
- Richard C Gershon (ORCID: https://orcid.org/0000-0003-0085-0112)
- Cindy J. Nowinski (ORCID: https://orcid.org/0000-0001-5608-909X)
- Maxwell Armand Mansolf (ORCID: https://orcid.org/0000-0001-6861-8657)
- Elizabeth M. Dworak (ORCID: https://orcid.org/0000-0003-4589-1663)
- Stephanie Ruth Young (ORCID: https://orcid.org/0000-0002-8205-9297)
- Yusuke Shono (ORCID: https://orcid.org/0000-0002-7006-1816)
Institutions
- Northwestern University (US)
- Claremont Graduate University (US)
Publication Details
- Journal
- Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring
- Published
- 2025-10-01
- DOI
- https://doi.org/10.1002/dad2.70219
- Citations
- 1
- Primary Topic
- Electronic Health Records Systems
- Type
- article
- Field-Weighted Citation Impact
- 2.94
Funders
- National Institutes of Health
- National Institute of Neurological Disorders and Stroke