  Alzheimer's disease (AD) is the fifth-leading cause of death among Americans
aged 65 and older. Screening and early detection of AD and related dementias
(ADRD) are critical for timely intervention and for identifying clinical trial
participants. The widespread adoption of electronic health records (EHRs)
offers an important resource for developing ADRD screening tools such as
machine learning based predictive models. Recent advancements in large language
models (LLMs) demonstrate their unprecedented capability of encoding knowledge
and performing reasoning, which offers them strong potential for enhancing risk
prediction. This paper proposes a novel pipeline that augments risk prediction
by leveraging the few-shot inference power of LLMs to make predictions on cases
where traditional supervised learning methods (SLs) may not excel.
Specifically, we develop a collaborative pipeline that combines SLs and LLMs
via a confidence-driven decision-making mechanism, leveraging the strengths of
SLs in clear-cut cases and LLMs in more complex scenarios. We evaluate this
pipeline using a real-world EHR data warehouse from Oregon Health \& Science
University (OHSU) Hospital, encompassing EHRs from over 2.5 million patients
and more than 20 million patient encounters. Our results show that our proposed
approach effectively combines the power of SLs and LLMs, offering significant
improvements in predictive performance. This advancement holds promise for
revolutionizing ADRD screening and early detection practices, with potential
implications for better strategies of patient management and thus improving
healthcare.
