Symptom-BERT: Enhancing Cancer Symptom Detection in EHR Clinical Notes.

CONTEXT

Extracting cancer symptom documentation allows clinicians to develop highly individualized symptom prediction algorithms to deliver symptom management care. Leveraging advanced language models to detect symptom data in clinical narratives can significantly enhance this process.

OBJECTIVE

This study uses a pretrained large language model to detect and extract cancer symptoms in clinical notes.

METHODS

We developed a pretrained language model to identify cancer symptoms in clinical notes based on a clinical corpus from the Enterprise Data Warehouse for Research at a healthcare system in the Midwestern United States. This study was conducted in 4 phases: pretraining a Bio-Clinical BERT model on one million unlabeled clinical documents, fine-tuning Symptom-BERT for detecting 13 cancer symptom groups within 1112 annotated clinical notes, generating 180 synthetic clinical notes using ChatGPT-4 for external validation, and comparing the internal and external performance of Symptom-BERT against a non-pretrained version and six other BERT implementations.

RESULTS

The Symptom-BERT model effectively detected cancer symptoms in clinical notes. It achieved results with a micro-averaged F1-score of 0.933, an AUC of 0.929 internally, and 0.831 and 0.834 externally. Our analysis shows that physical symptoms, like Pruritus, are typically identified with higher performance than psychological symptoms, such as anxiety.

CONCLUSION

This study underscores the transformative potential of specialized pretraining on domain-specific data in boosting the performance of language models for medical applications. The Symptom-BERT model's exceptional efficacy in detecting cancer symptoms heralds a groundbreaking stride in patient-centered AI technologies, offering a promising path to elevate symptom management and cultivate superior patient self-care outcomes.