First Multilingual clinical NLP workshop: MUCLIN (MIE2020/EFMI)

There is an increasing interest in exploiting clinical texts by means of language technologies and text mining approaches. Structured clinical information, in the form of codified clinical records relying on controlled vocabularies such as ICD10 is a key resource for statistical analysis techniques applied to patient data.

Clinical natural language processing (NLP) and AI-based document indexing can result in tools for automatic clinical coding by exploiting directly the unstructured content of EHRs. Such tools are playing an increasing role to generate results that do complement health informatics approaches focusing on translational medicine challenges, by providing relevant diagnostic information extracted from clinical narratives. This implies that text mining generated clinical coding results can provide a rich clinical context for patient health information necessary for other data analysis processes like bioinformatics and OMICS data exploration.

The workshop will include a panel discussion and short flash talks with experts on the role of shared tasks to promote clinical and biomedical NLP.

Some of the proposed topics for the discussion are: generation of shareable data collections for clinical NLP and automatic coding systems, use of AI and deep learning methods applied to clinical text mining, exploitation of unstructured content of EHRs for translational medicine, explainable IA strategies, evaluation metrics and scenarios for automatic clinical coding systems, multilingual clinical coding strategies and shared tasks.

CodiEsp setting and results were presented at the workshop First Multilingual clinical NLP workshop (MUCLIN) at MIE2020. The original MIE programme can be accessed here. This year, MIE was virtual thanks to the collaboration of EFMI and took place on July 4th, 2020.