标题： SHREC：一种用于通过大型语言模型推进下一代计算表型分析的框架 显示英文标题

标题： SHREC: A Framework for Advancing Next-Generation Computational Phenotyping with Large Language Models

摘要： 目标：计算表型是中心信息学活动，所产生的队列支持各种应用。 然而，由于手动数据审查、自动化有限以及在不同来源之间适应算法的困难，它耗时较长。 由于大型语言模型（LLMs）在文本分类、理解和生成方面表现出令人鼓舞的能力，我们认为它们在传统上由人类专家执行的重复性手动审查任务中表现良好。 为了支持下一代计算表型方法，我们开发了SHREC，这是一个将LLMs全面集成到端到端表型管道中的框架。 方法：我们应用并测试了三种轻量级LLMs（Gemma2 270亿参数，Mistral Small 240亿参数，Phi-4 140亿参数）使用先前开发的ARF呼吸支持疗法的表型来分类概念和表型患者。 结果：所有模型在概念分类方面表现良好，最佳模型（Mistral）在所有相关概念上的AUROC为0.896。 对于表型分析，模型对所有表型表现出接近完美的特异性，而表现最好的模型（Mistral）在单一疗法表型上的平均AUROC达到0.853，尽管在多疗法表型上的表现较低。 结论：当前的轻量级LLMs可以可行地协助研究人员完成资源密集型的表型任务，如手动数据审查。 LLMs有若干优势支持其在计算表型中的应用，例如仅通过提示工程即可适应新任务以及整合原始电子健康记录（EHR）数据的能力。 推进下一代表型方法的未来步骤包括确定整合生物医学数据的最佳策略，探索LLMs如何推理，并推进生成模型方法。 显示英文摘要

摘要： Objective: Computational phenotyping is a central informatics activity with resulting cohorts supporting a wide variety of applications. However, it is time-intensive because of manual data review, limited automation, and difficulties in adapting algorithms across sources. Since LLMs have demonstrated promising capabilities for text classification, comprehension, and generation, we posit they will perform well at repetitive manual review tasks traditionally performed by human experts. To support next-generation computational phenotyping methods, we developed SHREC, a framework for comprehensive integration of LLMs into end-to-end phenotyping pipelines. Methods: We applied and tested the ability of three lightweight LLMs (Gemma2 27 billion, Mistral Small 24 billion, and Phi-4 14 billion) to classify concepts and phenotype patients using previously developed phenotypes for ARF respiratory support therapies. Results: All models performed well on concept classification, with the best model (Mistral) achieving an AUROC of 0.896 across all relevant concepts. For phenotyping, models demonstrated near-perfect specificity for all phenotypes, and the top-performing model (Mistral) reached an average AUROC of 0.853 for single-therapy phenotypes, despite lower performance on multi-therapy phenotypes. Conclusion: Current lightweight LLMs can feasibly assist researchers with resource-intensive phenotyping tasks such as manual data review. There are several advantages of LLMs that support their application to computational phenotyping, such as their ability to adapt to new tasks with prompt engineering alone and their ability to incorporate raw EHR data. Future steps to advance next-generation phenotyping methods include determining optimal strategies for integrating biomedical data, exploring how LLMs reason, and advancing generative model methods.