6th Clinical Natural Language Processing Workshop (ClinicalNLP)

Clinical text is growing rapidly as electronic health records become pervasive. Much of the information recorded in a clinical encounter is located exclusively in provider narrative notes, which makes them indispensable for supplementing structured clinical data in order to better understand patient state and care provided. The methods and tools developed for the clinical domain have historically lagged behind the scientific advances in the general-domain NLP. Despite the substantial recent strides in clinical NLP, a substantial gap remains. The goal of this workshop is to address this gap by establishing a regular event in CL conferences that brings together researchers interested in developing state-of-the-art methods for the clinical domain. The focus is on improving NLP technology to enable clinical applications, and specifically, information extraction and modeling of narrative provider notes from electronic health records, patient encounter transcripts, and other clinical narratives.

Relevant topics for the workshop include, but are not limited to:

Modeling clinical text in standard NLP tasks (tagging, chunking, parsing, entity identification, entity linking/normalization, relation extraction, coreference, summarization, etc.)
De-identification and other handling of protected health information
Disease detection and other coding of clinical documents (e.g., ICD)
Structure of clinical documents (e.g., section identification)
Information extraction from clinical text
Integration of structured and textual data for clinical tasks
Domain adaptation and transfer learning techniques for clinical data
Generation of clinical notes: summarization, image-to-text, generation of notes from clinical conversations, etc.
Annotation schemes and annotation methodology for clinical data
Evaluation techniques for the clinical domain
Bias and fairness in clinical text