RT-Surv: Improving Mortality Prediction After Radiotherapy with Large Language Model Structuring of Large-Scale Unstructured Electronic Health Records

• URL: http://arxiv.org/abs/2408.05074v4
• Date: Fri, 13 Sep 2024 05:12:52 GMT
• Title: RT-Surv: Improving Mortality Prediction After Radiotherapy with Large Language Model Structuring of Large-Scale Unstructured Electronic Health Records
• Authors: Sangjoon Park, Chan Woo Wee, Seo Hee Choi, Kyung Hwan Kim, Jee Suk Chang, Hong In Yoon, Ik Jae Lee, Yong Bae Kim, Jaeho Cho, Ki Chang Keum, Chang Geol Lee, Hwa Kyung Byun, Woong Sub Koom,
• Abstract summary: This study explores the potential of large language models (LLMs) to structure unstructured electronic health record (EHR) data. Data from 34,276 patients treated with radiotherapy (RT) at Yonsei Cancer Center were analyzed. Survival prediction models were developed using statistical, machine learning, and deep learning approaches.
• Score: 2.608410928225647
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Accurate patient selection is critical in radiotherapy (RT) to prevent ineffective treatments. Traditional survival prediction models, relying on structured data, often lack precision. This study explores the potential of large language models (LLMs) to structure unstructured electronic health record (EHR) data, thereby improving survival prediction accuracy through comprehensive clinical information integration. Data from 34,276 patients treated with RT at Yonsei Cancer Center between 2013 and 2023 were analyzed, encompassing both structured and unstructured data. An open-source LLM was used to structure the unstructured EHR data via single-shot learning, with its performance compared against a domain-specific medical LLM and a smaller variant. Survival prediction models were developed using statistical, machine learning, and deep learning approaches, incorporating both structured and LLM-structured data. Clinical experts evaluated the accuracy of the LLM-structured data. The open-source LLM achieved 87.5% accuracy in structuring unstructured EHR data without additional training, significantly outperforming the domain-specific medical LLM, which reached only 35.8% accuracy. Larger LLMs were more effective, particularly in extracting clinically relevant features like general condition and disease extent, which closely correlated with patient survival. Incorporating LLM-structured clinical features into survival prediction models significantly improved accuracy, with the C-index of deep learning models increasing from 0.737 to 0.820. These models also became more interpretable by emphasizing clinically significant factors. This study shows that general-domain LLMs, even without specific medical training, can effectively structure large-scale unstructured EHR data, substantially enhancing the accuracy and interpretability of clinical predictive models.

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