AI & NLP in Biomedicine

The incremental adoption of electronic health records (EHR) as a key component for health systems raises a number of questions that remain partially unsolved. EHRs store information of heterogeneous nature in a wide variety of formats, including free-text documents, such as clinical notes or radiological reports, which contain information related to clinical diagnoses, treatments or procedures. However, the unstructured nature of these textual fields makes the task of automatically extracting relevant concepts from them especially difficult. In this sense, the transformation of clinical text---written in natural language---into structured data enables its use in tasks such as treatment planning, disease research or decision-making in clinical practice as well as in the management of health systems. In recent years, natural language processing (NLP), artificial intelligence (AI) techniques and, especially large language models (LLM), have been applied to problems such as clinical coding, automatic classification of clinical documents or named clinical-entities recognition (NER) and normalization (NEL), among others. However, most of the existing studies in the specific literature have only been carried out onto English texts, due to the scarce availability of annotated corpora with clinical-entity information or additional linguistic resources in other languages such as Spanish. In the AI4cats group, we aim to advance in the design and adapt new AI algorithms for NLP in the Biomedical domain and, more specificaly, in low-resource languages (such as Spanish) and settings, to be applied to information-processing downstream tasks that are carried out on unstructured textual data stored in EHRs.