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Xiaowei Xu^1,2, Ruixuan Jiang^3,4,5, Si Zheng^1,6

¹Institute of Medical Information, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100020 China.

Journal of healthcare informatics research

|February 3, 2025

概括

使用大型语言模型 (LLM) 的新模型DizzyInsight改善了慢性的分类,特别是在持续的姿势感知 (PPPD) 和焦虑障碍方面.

科学领域:

医疗信息学医疗信息学
医疗保健中的人工智能
神经学神经学

背景情况:

慢性头障碍的准确分类,包括持续的姿势感知头 (PPPD),焦虑和抑郁障碍,在初级保健中具有挑战性.
现有的诊断方法可能缺乏对慢性头复杂的病因学分类所需的精度.
大型语言模型 (LLM) 具有提高医学诊断准确性的潜力.

研究的目的:

推出DizzyInsight,这是慢性的创新病因学分类模型.
提高LLM和机器学习 (ML) 方法的准确性和可靠性,用于分类慢性头.
评估DizzyInsight在分类特定慢性头疾病方面的表现.

主要方法:

八位专家医生定义并评估了慢性头的特征.
收集了260名慢性头患者的数据集 (105名男性,155名女性,平均年龄为59.52±13岁).
开发了DizzyInsight模型,利用LLM,并与一般的LLM和BERT模型进行病因分类进行比较.

主要成果:

对于持续的姿势感知性头 (PPPD),DizzyInsight显示了0.69的积极预测值和0.86的灵敏度.
对于焦虑和抑郁症,DizzyInsight的预测值为0.81,敏感度为0.66.
使用LLM的DizzyInsight模型在慢性的病因分类中显示出更好的疗效和可解释性.

结论:

"DizzyInsight"显示了利用LLM进行慢性头疾病的精确病因分类的巨大潜力.
该模型的性能表明,对PPPD和焦虑/抑郁症等疾病的诊断能力有所改善.
需要进一步的研究来完善证据识别,评估初级保健的适用性,并验证外部有效性.

关键词:

焦虑和抑郁障碍焦虑和抑郁障碍慢性头慢性头支持决定的决定支持.病因学分类病因学分类持续的姿势感知性头 (PPPD)

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