用大模型与医学本体结合，自动识别西班牙语病历中的皮肤病
Automatic detection of diseases in Spanish clinical notes combining medical language models and ontologies
- 结合语言模型与医学本体，按部位、严重程度、顺序学习病理特征
- 在西班牙语病历上达到0.84精确率，0.82微F1，0.75宏F1
- 方法与数据集开源，适合医疗文本分析与多语言NLP研究者
本文提出一种混合方法，用于自动检测医学报告中的皮肤疾病。通过将大语言模型与医学本体结合，根据首次就诊或复诊报告预测患者可能患有的病理类型。实验表明，引导模型按部位、严重程度和学习顺序来理解病理特征，可显著提升准确率。该方法在医疗文本分类任务中达到0.84的精确率、0.82的微F1得分和0.75的宏F1得分，性能达当前最优水平。论文同时公开了所用方法与数据集，供社区使用。
原文摘要 · Abstract (English)
In this paper we present a hybrid method for the automatic detection of dermatological pathologies in medical reports. We use a large language model combined with medical ontologies to predict, given a first appointment or follow-up medical report, the pathology a person may suffer from. The results show that teaching the model to learn the type, severity and location on the body of a dermatological pathology, as well as in which order it has to learn these three features, significantly increases its accuracy. The article presents the demonstration of state-of-the-art results for classification of medical texts with a precision of 0.84, micro and macro F1-score of 0.82 and 0.75, and makes both the method and the data set used available to the community.
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