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.
Clinical text offers unique challenges that differentiate it not only from open-domain data, but from other types of text in the biomedical domain as well. Notably, clinical text contains a significant number of abbreviations, medical terms, and other clinical jargon. Clinical narratives are characterized by non-standard document structures that are often critical to overall understanding. Narrative provider notes are designed to communicate with other experts while at the same time serving as a legal record. Finally, clinical notes contain sensitive patient-specific information that raise privacy and security concerns that present special challenges for natural language systems.
We invite high-quality original submissions that develop methods to address the above challenges to NLP in the clinical domain. We are interested in work that specifically focuses on advancing the state-of-the-art in clinical NLP, rather than merely applies existing NLP systems to downstream clinical problems (such as outcome prediction or clinical cohort selection). The submissions may include initial results from promising new methods that may spark interest from other members of the Clinical NLP community and lead to collaborative work. The following is a list of topics of interest for this workshop:
All submissions must be in PDF format and should follow EMNLP 2020 style guidelines:
Submissions may have a maximum length of eight (8) pages for long papers and four (4) pages for short papers, with unlimited pages for references.
Both long and short papers will undergo rigorous review.
All submissions should be anonymized, and should not include authors' names or any other identifying information.
Please submit at the following site:
|Submissions due (both short and long)||Wednesday||July 15, 2020|
|Notification of acceptance||Monday||August 17, 2020|
|Camera-ready papers due||Monday||August 31, 2020|
|Workshop||Wednesday||Nov 11, 2020|