  Participant recruitment based on unstructured medical texts such as clinical
notes and radiology reports has been a challenging yet important task for the
cohort establishment in clinical research. Recently, Large Language Models
(LLMs) such as ChatGPT have achieved tremendous success in various downstream
tasks thanks to their promising performance in language understanding,
inference, and generation. It is then natural to test their feasibility in
solving the cohort recruitment task, which involves the classification of a
given paragraph of medical text into disease label(s). However, when applied to
knowledge-intensive problem settings such as medical text classification, where
the LLMs are expected to understand the decision made by human experts and
accurately identify the implied disease labels, the LLMs show a mediocre
performance. A possible explanation is that, by only using the medical text,
the LLMs neglect to use the rich context of additional information that
languages afford. To this end, we propose to use a knowledge graph as auxiliary
information to guide the LLMs in making predictions. Moreover, to further boost
the LLMs adapt to the problem setting, we apply a chain-of-thought (CoT) sample
selection strategy enhanced by reinforcement learning, which selects a set of
CoT samples given each individual medical report. Experimental results and
various ablation studies show that our few-shot learning method achieves
satisfactory performance compared with fine-tuning strategies and gains superb
advantages when the available data is limited. The code and sample dataset of
the proposed CohortGPT model is available at:
https://anonymous.4open.science/r/CohortGPT-4872/
