Development of a privacy preserving large language model for automated data extraction from thyroid cancer pathology reports

Abstract

Abstract Background Popularized by ChatGPT, large language models (LLM) are poised to transform the scalability of clinical natural language processing (NLP) downstream tasks such as medical question answering (MQA) and may enhance the ability to rapidly and accurately extract key information from clinical narrative reports. However, the use of LLMs in the healthcare setting is limited by cost, computing power and concern for patient privacy. In this study we evaluate the extraction performance of a privacy preserving LLM for automated MQA from surgical pathology reports. Methods 84 thyroid cancer surgical pathology reports were assessed by two independent reviewers and the open-source FastChat-T5 3B-parameter LLM using institutional computing resources. Longer text reports were converted to embeddings. 12 medical questions for staging and recurrence risk data extraction were formulated and answered for each report. Time to respond and concordance of answers were evaluated. Results Out of a total of 1008 questions answered, reviewers 1 and 2 had an average concordance rate of responses of 99.1% (SD: 1.0%). The LLM was concordant with reviewers 1 and 2 at an overall average rate of 88.86% (SD: 7.02%) and 89.56% (SD: 7.20%). The overall time to review and answer questions for all reports was 206.9, 124.04 and 19.56 minutes for Reviewers 1, 2 and LLM, respectively. Conclusion A privacy preserving LLM may be used for MQA with considerable time-saving and an acceptable accuracy in responses. Prompt engineering and fine tuning may further augment automated data extraction from clinical narratives for the provision of real-time, essential clinical insights.

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