Álvaro García-Barragán

The wide adoption of electronic health records (EHRs) offers immense potential as a source of support for clinical research. However, previous studies focused on extracting only a limited set of medical concepts to support information extraction in the cancer domain for the Spanish language. Building on the success of deep learning for processing natural language texts, this paper proposes a transformer-based approach to extract named entities from breast cancer clinical notes written in Spanish and compares several language models. To facilitate this approach, a schema for annotating clinical notes with breast cancer concepts is presented, and a corpus for breast cancer is developed. Results indicate that both BERT-based and RoBERTa-based language models demonstrate competitive performance in clinical Named Entity Recognition (NER). Specifically, BETO and multilingual BERT achieve F-scores of 93.71% and 94.63%, respectively. Additionally, RoBERTa Biomedical attains an F-score of 95.01%, while RoBERTa BNE achieves an F-score of 94.54%. The findings suggest that transformers can feasibly extract information in the clinical domain in the Spanish language, with the use of models trained on biomedical texts contributing to enhanced results. The proposed approach takes advantage of transfer learning techniques by fine-tuning language models to automatically represent text features and avoiding the time-consuming feature engineering process.

Using Natural Language Processing (NLP) in the clinical domain has increased the possibility of automatically extracting information from oncology clinical narratives. Specifically, deep learning methods have been used to extract information in the cancer domain. However, most of the above proposals have concentrated only on extracting named entities from clinical narratives, but those proposals do not include a methodology for structuring the information after an information extraction step. In this paper, we propose an automatic pipeline based on deep learning for structuring breast cancer information from clinical narratives written in Spanish. The pipeline inputs a set of clinical documents written in narrative form and automatically generates a structured JSON file that contains the information for each patient. This pipeline integrates both clinical entity extraction and negation and uncertainty detection. Obtained results have shown that deep learning methods are feasible for structuring information in the breast cancer domain.