Álvaro García-Barragán

In the rapidly evolving field of biomedical text mining, the extraction of phenotypic entities from unstructured texts remains a pivotal challenge. This paper introduces a novel method that leverage Large Language Models (LLMs) to extract phenotypical entities from freely available texts such as Wikipedia. Our approach goes beyond traditional Named Entity Recognition (NER) techniques by utilizing both local and cloud-based LLMs. We present a comprehensive comparison with state-of-the-art tools. Our study confirms the significant advantages of LLMs in identifying relevant phenotypic entities, thus enhancing the ability of researchers and clinicians to understand and respond to disease dynamics more effectively. Therefore, this work underscores the potential of next-generation LLMs to redefine the standards for the extraction of phenotypic entities in biomedical research.

Can newer Large Language Models (LLMs) like GPT be utilized without the need for extensive annotation, thereby enabling direct entity extraction? In this study, we explore this issue, comparing the efficacy of fine-tuning techniques with prompting methods to elucidate the potential of GPT in the identification of medical entities within Spanish electronic health records (EHR)

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.

The Bidirectional Encoder Representations from Transformers (BERT) has shown promising results in extracting information in the biomedical domain, including the cancer field. However, one of the challenges in the cancer domain is annotating resources to support information extraction. In this paper, we will show how models trained in a lung cancer corpus can be used to extract cancer concepts even in other cancer types. In particular, we will show the performance of BERT models on breast cancer data that was not used to train the models. Results are very promising as they show the possibility of applying deep learning-based models to predict cancer concepts in a different dataset to the one they were trained on, representing a considerable save of time and resources.