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

arXiv: https://arxiv.org/abs/2412.03176 · HTML: https://mlsanity.com/paper/2412.03176/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 69 references that are in the map.
- First posted on arXiv 4 Dec 2024.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [Neural Machine Translation: A Review and Survey](https://mlsanity.com/paper/1912.02047/) (2019)
- [Spanish Pre-trained BERT Model and Evaluation Data](https://mlsanity.com/paper/2308.02976/) (2023)
- [Multi-domain Clinical Natural Language Processing with MedCAT: the Medical Concept Annotation Toolkit](https://mlsanity.com/paper/2010.01165/) (2020)
- [BiomedRAG: A Retrieval Augmented Large Language Model for Biomedicine](https://mlsanity.com/paper/2405.00465/) (2024)
- [BERTIN: Efficient Pre-Training of a Spanish Language Model using Perplexity Sampling](https://mlsanity.com/paper/2207.06814/) (2022)
- [Top-k Multiclass SVM](https://mlsanity.com/paper/1511.06683/) (2015)
- [MedGPT: Medical Concept Prediction from Clinical Narratives](https://mlsanity.com/paper/2107.03134/) (2021)
- [Sequential Diagnosis Prediction with Transformer and Ontological Representation](https://mlsanity.com/paper/2109.03069/) (2021)
- [Where are we in semantic concept extraction for Spoken Language Understanding?](https://mlsanity.com/paper/2106.13045/) (2021)
- [Detecting of a Patient's Condition From Clinical Narratives Using Natural Language Representation](https://mlsanity.com/paper/2104.03969/) (2021)
- [Natural Language Processing in Biomedicine: A Unified System Architecture Overview](https://mlsanity.com/paper/1401.0569/) (2014)
- [Trade-offs in Top-k Classification Accuracies on Losses for Deep Learning](https://mlsanity.com/paper/2007.15359/) (2020)

## Abstract (arXiv)

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.