Extraction of Normalized Symptom Mentions From Clinical Narratives Using Large Language Models.

Abstract

Symptoms, or subjective experiences of patients which can indicate underlying pathology, are important for guiding clinician decision-making and revealing patient wellbeing. However, they are difficult to study because information is primarily found in clinical free text, not in structured electronic health record fields. This study finds that large language models (LLMs) can extract several common symptom concepts from clinical narratives, using an approach of including clarifying information in the prompt, few-shot examples, and chain-of-thought-prompting. This approach is compared to symptom-specific machine learning classifiers based on clinical concepts mapped from free text. For most symptom concepts, the LLM performs better and achieves a higher F1-score, likely by leveraging context important for the symptom normalization task. Unlocking information about symptom concepts from clinical narratives has potential to improve healthcare workflows and facilitate a broad range of research agendas.