基于自我报告的化学感官描述的COVID-19诊断的基于文本的预测
Hongyang Li1, Richard C Gerkin2,3, Alyssa Bakke4
1Health Care and Life Sciences, IBM T.J. Watson Research Center, Yorktown Heights, NY, USA.
Communications medicine
|July 27, 2023
概括
这项研究表明,使用大语言模型 (LLM) 的自然语言处理 (NLP) 可以从嗅觉和味觉变化的描述中预测COVID-19. 这种方法与诊断病毒感染的传统秤一样有效.
科学领域:
- 计算语言学计算语言学
- 机器学习在医疗保健中的应用.
- 感官感知和疾病的感知.
背景情况:
- 人类语言在描述嗅觉和味觉等感官体验时所感知的局限性.
- 对于COVID-19等传染病需要可靠的诊断工具.
研究的目的:
- 开发和评估使用自然语言处理 (NLP) 和大型语言模型 (LLM) 的机器学习模型.
- 预测COVID-19诊断仅基于急性化学感官变化的文本描述.
- 评估患者报告的感觉症状描述对疾病诊断的有用性.
主要方法:
- 开发了一种NLP模型,利用LLM来分析文本数据.
- 训练模型的数据集超过1500个调查答案描述了气味,味道和化学反应的变化.
- 在COVID-19大流行早期 (2020年春季) 收集的数据.
主要成果:
- 在预测COVID-19诊断时,NLP模型实现了接收器运行特征曲线 (AUC ROC) 下的区域大约为0.65.
- 模型的性能与使用量化评级尺度对自我报告的功能变化的传统方法相比.
- 该模型确定的关键预测因素包括与感官相关的描述词 (例如",气味"",味道"",感觉") 和特定的味道/气味形容词 (例如","",甜"",辛"",酸").
结论:
- 描述与病毒感染相关的感知症状可以有效地微调LLM以进行准确的诊断预测.
- 该模型展示了使用患者生成的文本数据来解释诊断状态的潜力.
- 未来的应用可能涉及分析来自在线健康平台和电子健康记录的患者叙述.
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