Testing machine learning of multimodal digital markers for early detection of cognitive impairment in Alzheimer's Disease rhoda
Our findings present an ongoing effort on the potential of explainable AI to validate digital measures to identify those with MCI. While the current model effectively identifies prevalent non-transitioners, it remains limited in identifying prevalent transitioners. Future research will focus on refining model sensitivity and balancing classification performance.
Authors
- Christian Cumbaa
- R. Au
- Kristi Ho (ORCID: https://orcid.org/0000-0002-2576-9831)
- Luca Pani
- Edward Searls
- Alexa Burk (ORCID: https://orcid.org/0009-0006-1545-5456)
- Michael L. Alosco
- Jesse Mez
- Larry Alphs
- Mike Tsay
- Joseph Geraci
- Bessi Qorri
Institutions
- Boston University (US)
- University of Modena and Reggio Emilia (IT)
- University of Miami (US)
- Queen's University (CA)
- University of California San Diego (US)
- Framingham Heart Study (US)
Publication Details
- Journal
- Alzheimer s & Dementia
- Published
- 2025-12-01
- DOI
- https://doi.org/10.1002/alz70856_107581
- Primary Topic
- Dementia and Cognitive Impairment Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00