Automatic detection of diseases in Spanish clinical notes combining medical language models and ontologies
This addresses the problem of automating disease detection from clinical notes for medical professionals, though it appears incremental as it applies existing hybrid methods to a specific domain.
The paper tackles automatic detection of dermatological pathologies in Spanish clinical notes by combining a large language model with medical ontologies, achieving state-of-the-art results with a precision of 0.84 and micro/macro F1-scores of 0.82/0.75.
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.