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.
@article{arxiv.2412.03176,
title = {Automatic detection of diseases in Spanish clinical notes combining medical language models and ontologies},
author = {Leon-Paul Schaub Torre and Pelayo Quiros and Helena Garcia Mieres},
journal= {arXiv preprint arXiv:2412.03176},
year = {2024}
}