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让我们问病人:基于患者在自由文本中的症状描述的疾病预测

Inés Pérez-Sancristóbal¹, Nils Steinz², Ling Qin²

¹Faculty of Medicine, Complutense University of Madrid, Madrid, Spain.

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概括

通过自然语言处理 (NLP) 和机器学习 (ML) 分析患者的症状描述,有望改善类风湿性疾病的诊断决策. 这种人工智能辅助的方法可以减少诊断模两可和不必要的转诊.

科学领域:

类风湿病学类风湿病学
医疗信息学医疗信息学
人工智能在医学中的应用

背景情况:

精确诊断骨关节炎 (OA),纤维肌痛 (FM) 和免疫媒介性类风湿性疾病 (imRD) 对于有效的患者管理至关重要.
利用患者报告的症状数据可以提高诊断准确度.

研究的目的:

评估自我报告的自由文本症状描述对支持OA,FM和imRD诊断决策的有用性.
评估自然语言处理 (NLP) 和机器学习 (ML) 模型在分析这些描述中的性能.

主要方法:

用TF-IDF矢量化和支持矢量机 (SVM) 分类分析了8454个患者自由文本描述的数据集.
模型针对特异性 (OA,FM) 或灵敏性 (imRD) 进行了优化,并在一个独立的数据集上进行了验证.
为了模型的可解释性,使用了夏普利添加式解释 (SHAP).

主要成果:

SVM模型显示了中度的诊断支持 (AUC-ROC:OA 0.68,FM 0.75,imRD 0.69).
优化的模型实现了高特异性 (OA 0.82,FM 0.92),减少了误诊和不必要的转诊.
imRD模型实现了高灵敏度 (0.92) 和NPV (0.77),最大限度地减少了错过的诊断.
SHAP分析证实了语言模式与临床推理之间的一致性.

结论:

通过NLP/ML分析的患者报告的数据可以增强类风湿病学决策.
对自由文本症状的人工智能辅助分析显示,有可能减少类风湿性疾病的诊断模两可.

关键词:

诊断诊断诊断诊断诊断诊断纤维肌痛是一种纤维肌痛.免费的文本分析.免疫介导的类风湿性疾病.机器学习是机器学习.自然语言处理自然语言处理.骨关节炎是一种关节炎.类风湿病学类风湿病学症状描述症状描述症状描述

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