Large Language Model Symptom Identification From Clinical Text: Multicenter Study.

Area of Science:

• Medical Informatics
• Artificial Intelligence in Healthcare
• Clinical Natural Language Processing

Background:

• Patient symptom recognition is crucial for medicine, research, and public health.
• Symptoms are often underreported in coded data despite being documented in physician notes.
• Large language models (LLMs) show potential for identifying symptoms by mimicking human chart reviewers.

Purpose of the Study:

• To measure the accuracy of LLMs in identifying infectious respiratory disease symptoms based on chart review guidelines.
• To evaluate the generalizability of LLMs across different healthcare sites without site-specific adjustments.

Main Methods:

• Four LLMs (GPT-4, GPT-3.5, Llama2 70B, Mixtral 8×7B) were prompted to act as chart reviewers.
• LLM performance was optimized using a development corpus and tested against expert-annotated ground truth.
• LLM generalizability was assessed using a validation corpus from multiple emergency departments.

Main Results:

• All tested LLMs significantly outperformed the International Classification of Diseases, Tenth Revision (ICD-10)-based method (F1-score=45.1%).
• GPT-4 achieved the highest accuracy (F1-score=91.4%) and demonstrated superior generalizability in the validation corpus (F1-score=94.0%), outperforming the ICD-10 method (F1-score=26.9%).
• LLMs showed high accuracy in identifying symptoms, with GPT-4 significantly outperforming other models and the baseline method.

Conclusions:

• LLMs significantly enhance respiratory symptom identification in electronic health records compared to ICD-10 methods.
• GPT-4 exhibits high accuracy and generalizability, suggesting potential for augmenting or replacing traditional symptom identification approaches.
• LLMs can effectively mimic human chart reviewers for symptom identification, warranting further investigation into broader symptom types and clinical settings.