CARE-AD: An Advanced Multi-Agent Language Model Framework for Predicting Alzheimer’s Disease Through Longitudinal Clinical Notes

This study utilized the VHA Corporate Data Warehouse’s extensive EHR database, covering 2000-2022, to investigate Alzheimer’s disease (AD) using a case-control design. Approved by the US Veterans Affairs Institutional Review Board, it sought to identify early predictive markers for AD through stringent diagnostic criteria against AD-specific ICD codes. The cohort was meticulously constructed with 17,488 AD cases and 64,691 matched controls without dementia, ensuring clinical accuracy by excluding non-AD dementia. Utilizing a multi-agent framework, domain-specific agents analyzed longitudinal clinical notes, identifying significant symptom categories such as cognitive impairment and functional decline. Employing the fine-tuned LLaMA 3.1 model, the study aimed to develop a data extraction agent for effective classification of AD symptoms. Evaluation metrics were grounded in real-world clinical scenarios, reinforcing the study’s commitment to uncover early risk indicators, ultimately enhancing predictive accuracy and treatment planning for Alzheimer’s patients.

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