Automatic detection of diseases in Spanish clinical notes combining medical language models and ontologies

TL;DR

This paper introduces a hybrid approach combining medical language models and ontologies to automatically detect dermatological diseases in Spanish clinical notes, achieving state-of-the-art classification accuracy.

Contribution

It presents a novel hybrid method that leverages language models and ontologies for improved disease detection in medical reports, with detailed learning strategies for pathology features.

Findings

Precision of 0.84 in disease classification

Micro F1-score of 0.82, macro F1-score of 0.75

State-of-the-art results demonstrated

Abstract

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.