Acoustic Analysis of Primary Care Patient-provider Conversations to Screen for Cognitive Impairment
ML models trained on acoustic features from brief clinical conversations identified CI with high accuracy. These findings support the feasibility of passive, speech-based screening during routine primary care.
Authors
- Alex D. Federman (ORCID: https://orcid.org/0000-0002-4824-064X)
- Joseph T. Colonel (ORCID: https://orcid.org/0009-0000-5516-9231)
- J.P. Wisnivesky
- Lili Chan (ORCID: https://orcid.org/0000-0003-4300-5760)
- Tielman Van Vleck (ORCID: https://orcid.org/0000-0002-4488-8174)
- Jacqueline Becker (ORCID: https://orcid.org/0000-0003-2708-6046)
- Cara Faherty (ORCID: https://orcid.org/0009-0008-6645-794X)
- Laura Curtis
- Baihan Lin
Institutions
- Northwestern University (US)
- Icahn School of Medicine at Mount Sinai (US)
Publication Details
- Journal
- medRxiv
- Published
- 2025-12-29
- DOI
- https://doi.org/10.64898/2025.12.27.25343088
- Primary Topic
- Dementia and Cognitive Impairment Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Sanofi
- Regeneron Pharmaceuticals
- Georgia Clinical and Translational Science Alliance
- National Institutes of Health
- National Center for Advancing Translational Sciences