2026-08-29T14:55:01Zhttps://repisalud.isciii.es/rest/oai/request

oai:repisalud.isciii.es:20.500.12105/183442024-11-28T14:54:00Zcom_20.500.12105_15322com_20.500.12105_2051col_20.500.12105_16927

00925njm 22002777a 4500 dc López-García, Guillermo author Jerez, José M. author Ribelles, Nuria author Alba, Emilio author Veredas, Francisco J. author 2021-05-13 Automatic clinical coding is an essential task in the process of extracting relevant information from unstructured documents contained in electronic health records (EHRs). However, most research in the development of computer-based methods for clinical coding focuses on texts written in English due to the limited availability of medical linguistic resources in languages other than English. With nearly 500 million native speakers, there is a worldwide interest in processing healthcare texts in Spanish. In this study, we sys tematically analyzed transformer-based models for automatic clinical coding in Spanish. Using a transfer learning-based approach, the three existing transformer architectures that support the Spanish language, namely, multilingual BERT (mBERT), BETO and XLM-RoBERTa (XLM-R), were first pretrained on a corpus of real-world oncology clinical cases with the goal of adapting transformers to the particularities of Spanish medical texts. The resulting models were fine-tuned on three distinct clinical coding tasks, following a multilabel sentence classification strategy. For each analyzed transformer, the domain-specific version out performed the original general domain model across those tasks. Moreover, the combination of the developed strategy with an ensemble approach leveraging the predictive capacities of the three distinct transformers yielded the best obtained results, with MAP scores of 0.662, 0.544 and 0.884 on CodiEsp-D, CodiEsp-P and Cantemist-Coding shared tasks, which remarkably improved the previous state-of-the-art performance by 11.6%, 10.3% and 4.4%, respectively. We publicly release the mBERT, BETO and XLMR transform ers adapted to the Spanish clinical domain at https://github.com/guilopgar/ClinicalCodingTransformerES, providing the clinical natural language processing community with advanced deep learning methods for performing medical coding and other tasks in the Spanish clinical domain. 10.1109/ACCESS.2021.3080085 2169-3536 IEEE Access http://hdl.handle.net/10668/3836 http://hdl.handle.net/20.500.12105/18344 Clinical coding Deep learning Natural language processing Text classification Transformers Codificación clínica Aprendizaje profundo Procesamiento de lenguaje natural Modelos epidemiológicos Análisis y desempeño de tareas Transformers for Clinical Coding in Spanish