  The infrequency and heterogeneity of clinical presentations in rare diseases
often lead to underdiagnosis and their exclusion from structured datasets. This
necessitates the utilization of unstructured text data for comprehensive
analysis. However, the manual identification from clinical reports is an
arduous and intrinsically subjective task. This study proposes a novel hybrid
approach that synergistically combines a traditional dictionary-based natural
language processing (NLP) tool with the powerful capabilities of large language
models (LLMs) to enhance the identification of rare diseases from unstructured
clinical notes. We comprehensively evaluate various prompting strategies on six
large language models (LLMs) of varying sizes and domains (general and
medical). This evaluation encompasses zero-shot, few-shot, and
retrieval-augmented generation (RAG) techniques to enhance the LLMs' ability to
reason about and understand contextual information in patient reports. The
results demonstrate effectiveness in rare disease identification, highlighting
the potential for identifying underdiagnosed patients from clinical notes.
