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.

翻译：本文提出一种用于医学报告中皮肤病自动检测的混合方法。我们采用大型语言模型结合医学本体论，根据初诊或随访医疗报告预测患者可能罹患的病理类型。研究结果表明，通过指导模型学习皮肤病的类型、严重程度及身体部位特征，并规范这三类特征的学习顺序，能显著提升模型准确率。本文展示了当前最先进的医学文本分类结果：精确率达0.84，微观与宏观F1分数分别为0.82和0.75，同时向学术界公开了本方法及所使用的数据集。