探索大型语言模型在医疗保健中的潜力:专注于心血管疾病分析
Aihua Li1, Xinran Bi1, Sifan Chen1
1School of Management Science and Engineering, Central University of Finance and Economics, Changping District, Beijing, 102206 People's Republic of China.
Health information science and systems
|February 3, 2026
概括
大型语言模型 (LLM) 在医疗保健中显示出强大的潜力,用于使用各种患者数据进行疾病预测和风险评估. 与传统方法相比,这些AI模型提供了更好的交互性和概括性.
科学领域:
- 人工智能在医学中的应用
- 机器学习用于医疗保健
- 医疗研究中的大数据分析
背景情况:
- 大数据和人工智能技术的快速发展推动了各个领域的大型语言模型 (LLM) 的应用.
- 在医疗保健中,利用广泛的患者数据和医疗记录对于提高诊断准确性和预测疾病风险至关重要.
研究的目的:
- 建立一个研究框架,用于评估评估模型,特别是大型语言模型 (LLM),分析结构化和非结构化医疗数据.
- 探索LLM在医疗保健领域的潜在应用和性能.
主要方法:
- 提出了医疗保健大型语言模型潜在评估框架 (HLLM-潜在框架) 来评估医学数据分析中的LLM适用性.
- 通过使用标准化的心血管数据集 (结构化和非结构化) 与传统的机器学习和深度学习模型进行比较实验.
- 训练并配置现有的LLM用于疾病预测和健康风险评估任务.
主要成果:
- 大型语言模型有效地处理结构化和非结构化医疗数据,用于疾病预测和健康风险评估.
- 在结构化心血管疾病预测 (心力衰竭) 方面,LLMs实现了超过80%的准确性和回忆.
- 多模式LLM Janus Pro 7B在非结构化的心电图像分类中表现出85%的准确率.
结论:
- 与传统模型相比,大型语言模型表现出增强的交互性,概括性和减少对数据质量和功能工程的依赖性.
- 在医疗保健领域,LLM具有显著的优势,包括高效率,灵活性和多式联络整合能力.
- 预计LLM将在医疗和医疗保健应用中得到广泛采用,这是由于其已证明的潜力.
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