High-Throughput Phenotyping of the Symptoms of Alzheimer Disease and Related Dementias Using Large Language Models: Cross-Sectional Study
These results highlight the accuracy and reliability of our high-throughput ADRD phenotyping algorithm. By enabling automated symptom extraction, our approach has the potential to assist with differential diagnosis, as well as facilitate clinical trials and research studies of dementia.
Authors
- Colin Magdamo (ORCID: https://orcid.org/0000-0001-8965-4630)
- Michael Brandon Westover (ORCID: https://orcid.org/0000-0003-4803-312X)
- Sudeshna Das (ORCID: https://orcid.org/0000-0002-9486-6811)
- You Cheng (ORCID: https://orcid.org/0000-0002-3141-0104)
- John R. Dickson (ORCID: https://orcid.org/0000-0003-0135-7928)
- Shibani S. Mukerji (ORCID: https://orcid.org/0000-0002-5677-6954)
- Yingnan He
- Arjun Singh (ORCID: https://orcid.org/0009-0005-4370-3077)
- Mrunal Malekar
- Apoorva Bommareddy (ORCID: https://orcid.org/0009-0003-8920-8608)
Institutions
- Harvard University (US)
- Beth Israel Deaconess Hospital (US)
- Massachusetts General Hospital (US)
Publication Details
- Journal
- JMIR AI
- Published
- 2025-06-03
- DOI
- https://doi.org/10.2196/66926
- Citations
- 3
- Primary Topic
- Dementia and Cognitive Impairment Research
- Type
- article
- Field-Weighted Citation Impact
- 4.41