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耳鼻喉科的创新使用LLM用于早期检测睡眠呼吸障碍

Jin Zhou¹, Xiaoqin Li¹, Qianjun Xia²

¹Otolaryngology-Head and Neck Surgery/Sleep Medicine Center, West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, PR China.

SLAS technology

|March 23, 2025

概括

这项研究引入了一种混合机器学习 (ML) 和大语言模型 (LLM) 框架,用于早期识别睡眠呼吸障碍 (SDB),包括阻塞性睡眠呼吸暂停 (OSA),在查患者中实现高准确性.

科学领域:

耳鼻喉科耳鼻喉科耳鼻喉科
医疗信息学医疗信息学
医疗保健中的人工智能

背景情况:

睡眠呼吸障碍 (SDB),包括阻塞性睡眠呼吸暂停 (OSA),由于睡眠期间的呼吸道阻塞,造成了严重的健康风险.
早期检测和干预对于改善患者在SDB管理中的结果至关重要.
电子健康记录 (EHR) 包含用于识别SDB的有价值数据,但需要先进的分析方法.

研究的目的:

开发和评估一种新的混合机器学习 (ML) 和大型语言模型 (LLM) 框架,用于使用EHR数据早期识别睡眠呼吸障碍 (SDB).
通过将动态海搜索 (DSS) 算法与LLMs集成,提高SDB选的准确性和稳定性.
探索LLM在分析文本临床数据中的应用,以改进SDB检测.

主要方法:

开发了一个称为DSS-LLM的混合ML框架,将Dynamic Seagull Search (DSS) 算法与Large Language Models (LLMs) 结合起来.
使用了广泛的临床数据集,包括人口统计,病史,睡眠习惯,并发症和身体测量.
数据预处理涉及NLP技术和规范化,其次是主要组件分析 (PCA) 用于特征提取和选择.

主要成果:

与之前的模型相比,拟议的DSS-LLM模型在SDB分类方面表现出优异的性能.
实现了98.91%的分类准确度,98.9%的精度,98.9%的回忆,98.92%的回忆,以及98.58%的F1得分.
为LLM优化整合DSS显著提高了分类准确性和模型稳定性.

结论:

DSS-LLM框架为阻断性睡眠呼吸暂停 (OSA) 的临床前查提供了一种新且有效的解决方案.
这种混合方法提高了临床判断力,预计将导致耳鼻喉科患者的治疗结果更好.
该研究强调了将LLM等先进AI模型与传统ML集成为复杂的医学诊断的潜力.

关键词:

动态海搜索算法驱动的大型语言模型 (DSS-LLM)早期检测早期检测自然语言处理 (NLP)阻塞性睡眠呼吸暂停 (OSA) 是一种疾病.睡眠呼吸障碍 (SDB) 是一种睡眠呼吸障碍.

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