Streamlining Eligibility Assessment for Alzheimer’s Disease-Modifying Therapies: Prediction of MMSE Scores Using the Digital Clock and Recall
Machine learning can leverage multimodal features from the DCR to accurately and equitably crosswalk to MMSE scores in support of current guidelines, transforming a time-intensive manual test into a rapid, automated assessment. By deploying this "digital triage" engine, where traditional assessments are still used for DMT eligibility, healthcare systems can streamline the identification of DMT-eligible patients, reduce specialist referral bottlenecks, and ensure that access to life-altering therapies is determined by pathology rather than demography.
Authors
- Emma Chen (ORCID: https://orcid.org/0000-0002-7528-5229)
- Ali Seyed Jannati
- John Showalter (ORCID: https://orcid.org/0009-0002-6290-9589)
- Sean Tobyne (ORCID: https://orcid.org/0000-0001-5985-7920)
- Claudio Toro‐Serey (ORCID: https://orcid.org/0000-0002-5273-2276)
- Marissa Ciesla (ORCID: https://orcid.org/0000-0002-8424-324X)
- David Bates
- Alvaro Pascual-Leone
Institutions
- SIL International (US)
- Harvard University (US)
- Lemuel Shattuck Hospital (US)
- Hebrew SeniorLife (US)
Publication Details
- Journal
- medRxiv
- Published
- 2026-03-04
- DOI
- https://doi.org/10.64898/2026.03.03.26347542
- Primary Topic
- Dementia and Cognitive Impairment Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Alzheimer's Drug Discovery Foundation
- BrightFocus Foundation
- National Institutes of Health