Using Large Language Models to Detect Anxiety and Nausea/Vomiting Documentation in Clinical Notes of Patients With Cancer

Large language models (LLMs) are increasingly utilized for named entity recognition (NER) in health care, with significant potential to enhance symptom detection within electronic health records (EHRs). This study explores the application of LLMs to identify symptoms of anxiety and nausea/vomiting documented in the clinical notes of patients with cancer. We analyzed clinical notes from 8,490 patients diagnosed with various cancer types. Bio Clinical BERT and Bio GPT models were further pretrained on clinical text from this dataset. Two modeling strategies, fine-tuning and prompt-based learning, were implemented using Symptom-BERT and Symptom-GPT frameworks. Model performance was evaluated using F1 scores, emphasizing recognizing psychological symptoms (anxiety) and physical symptoms (nausea/vomiting). Fine-tuning with Symptom-BERT achieved the highest F1 scores, 0.989 for nausea/vomiting and 0.912 for anxiety, significantly outperforming Symptom-GPT in detection accuracy. While prompt-based learning with Symptom-GPT surpassed that of a few-shot learning, it remained less effective than fine-tuning. Fine-tuning excelled in identifying well-documented symptoms, particularly physical ones like nausea/vomiting. Using named entity recognition (NER), the study analyzed the entire dataset, detecting anxiety in 2,436 patients (28.69%) and nausea/vomiting in 3,338 patients (39.31%). While both fine-tuning and prompt-based learning approaches offer utility, fine-tuning demonstrates superior accuracy in recognizing symptoms from clinical narratives, particularly physical ones. LLM-based symptom detection can support oncology nurses and care teams by enabling earlier recognition of patient-reported symptoms documented in narrative notes. These tools offer practical value in improving symptom monitoring, care planning, and timely intervention, thereby enhancing patient-centered care in oncology settings.