A considerable amount of medically relevant information is hidden in large unstructured heterogeneous data collections, such as the medical literature, medicinal patents, electronic health records or specialized web-content (health blogs, patient forums or information generated by scientific and medical societies).  To process more efficiently medical big data there is a growing interest in exploiting natural language processing and text mining approaches, in particularly deep learning and artificial intelligence-based strategies.

A considerable amount of medically relevant information is hidden in large unstructured heterogeneous data collections, such as the medical literature, medicinal patents, electronic health records or specialized web-content (health blogs, patient forums or information generated by scientific and medical societies).  To process more efficiently medical big data there is a growing interest in exploiting natural language processing and text mining approaches, in particularly deep learning and artificial intelligence-based strategies.

The aim of the Plan de Impulso de las Tecnologías del Lenguaje (Plan TL), the Spanish national Plan for the Advancement of Language Technology, is to promote the development of resources of critical importance for processing textual data in Spanish as well as Catalan, Basque and Galician. The Health and biomedical domain constitute one of the flagship topics of the Spanish Plan TL.

To promote the development of health-related language technology applications, the Plan TL is both developing and identifying resources of key relevance including individual components/libraries, terminological resources, annotated corpora and annotation guidelines, as well as document collections and language models.