The electronic medical record (EMR) is a rich source of clinical information. However, unstructured textual data in the EMR presents many challenges to extract the potential hidden knowledge.
Clinical researchers leverage this information, but they require of methods to extract the knowledge. Despite Natural language processing (NLP) techniques have shown effective still several issues require research and further development such as detection of negation and uncertainty, multilingualism, normalization of concepts one extracted and extracting dates and joining it to medical concepts.

In this track we will deep on the challenges and advances. Contributions are welcome on the following areas to name some:
1.Deep Learning approaches for NLP
2.Name entity approaches
3.Natural history reconstruction
4.Dates and concepts mapping
5.Normalization of concepts
7.Negation and uncertainty detection
8.Rule based approaches
9.Federated learning

Organizer: Ernestina Menasalvas Ruiz