Negation-based transfer learning for improving biomedical Named Entity Recognition and Relation Extraction.

Background And Objectives: Named Entity Recognition (NER) and Relation Extraction (RE) are two of the most studied tasks in biomedical Natural Language Processing (NLP). The detection of specific terms and entities and the relationships between them are key aspects for the development of more complex automatic systems in the biomedical field. In this work, we explore transfer learning techniques for incorporating information about negation into systems performing NER and RE.

A keyphrase-based approach for interpretable ICD-10 code classification of Spanish medical reports.

Background And Objectives: The 10th version of International Classification of Diseases (ICD-10) codification system has been widely adopted by the health systems of many countries, including Spain. However, manual code assignment of Electronic Health Records (EHR) is a complex and time-consuming task that requires a great amount of specialised human resources. Therefore, several machine learning approaches are being proposed to assist in the assignment task.

Deep neural models for extracting entities and relationships in the new RDD corpus relating disabilities and rare diseases.

Background And Objective: There is a huge amount of rare diseases, many of which have associated important disabilities. It is paramount to know in advance the evolution of the disease in order to limit and prevent the appearance of disabilities and to prepare the patient to manage the future difficulties. Rare disease associations are making an effort to manually collect this information, but it is a long process.