# Detecci'on Autom'atica de Patolog'ias en Notas Cl'inicas en Espa~nol Combinando Modelos de Lenguaje y Ontolog'ias M'edicos

arXiv: https://arxiv.org/abs/2410.00616 · HTML: https://mlsanity.com/paper/2410.00616/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 75 references that are in the map.
- First posted on arXiv 1 Oct 2024.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [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)
- [Multi-domain Clinical Natural Language Processing with MedCAT: the Medical Concept Annotation Toolkit](https://mlsanity.com/paper/2010.01165/) (2020)
- [Hi-BEHRT: Hierarchical Transformer-based model for accurate prediction of clinical events using multimodal longitudinal electronic health records](https://mlsanity.com/paper/2106.11360/) (2021)
- [CPLLM: Clinical Prediction with Large Language Models](https://mlsanity.com/paper/2309.11295/) (2023)
- [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)
- [Combining Machine Learning and Ontology: A Systematic Literature Review](https://mlsanity.com/paper/2401.07744/) (2024)

## 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 dataset used available to the community. -- En este art'iculo presentamos un m'etodo h'ibrido para la detecci'on autom'atica de patolog'ias dermatol'ogicas en informes m'edicos. Usamos un modelo de lenguaje amplio en espa~nol combinado con ontolog'ias m'edicas para predecir, dado un informe m'edico de primera cita o de seguimiento, la patolog'ia del paciente. Los resultados muestran que el tipo, la gravedad y el sitio en el cuerpo de una patolog'ia dermatol'ogica, as'i como en qu'e orden tiene un modelo que aprender esas tres caracter'isticas, aumentan su precisi'on. El art'iculo presenta la demostraci'on de resultados comparables al estado del arte de clasificaci'on de textos m'edicos con una precisi'on de 0.84, micro y macro F1-score de 0.82 y 0.75, y deja a disposici'on de la comunidad tanto el m'etodo como el conjunto de datos utilizado.