Call for Papers & Participation
Website: https://clinical-nlp.github.io/2023/
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 provided care. 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 a wide variety of clinical applications by modeling narrative provider notes from electronic health records, patient encounter transcripts, and other clinical narratives.
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:
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
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
*** Special Tracks ***
Clinical NLP 2023 encourages submissions from four special tracks:
Clinical NLP in languages other than English
Clinical NLP in low-resource settings
Clinical NLP for clinical conversations (e.g., doctor-patient)
Multi-word expressions in Clinical NLP, a joint track with MWE 2023
Clinical NLP 2023 is hosting the MEDIQA-Chat shared tasks on the summarization of medical conversations for clinical note creation (Dialogue2Note) and the generation of synthetic doctor-patient conversations for relevant data creation/augmentation (Note2Dialogue):
Dialogue2Note Summarization. Given a conversation between a doctor and patient, participants are tasked with producing a clinical note summarizing the conversation with one or multiple note sections (e.g. Assessment, Past Medical History, Past Surgical History).
Note2Dialogue Generation. Given a clinical note, participants are tasked with generating a synthetic doctor-patient conversation related to the information described in the clinical note section(s).
Please visit the shared task website to register to participate and for additional information about the shared tasks.
Submissions may have a maximum length of eight (8) pages for long papers and four (4) pages for short papers and shared task participant papers, with unlimited pages for references and appendices. All submissions must be made through OpenReview and follow ACL formatting guidelines.
The OpenReview submission site can be found here: OpenReview-ClinicalNLP
We encourage submissions of papers submitted to but not accepted by EACL 2023, ACL 2023, or ACL Rolling Review, as long as the topics are relevant to Clinical NLP.
Shared Tasks:
Registration opens: January 10, 2023
Release of the training and validation sets: February 10, 2023
Release of the test sets: March 15, 2023
Run submission deadline: March 17, 2023
Release of the official results: March 31, 2023
Workshop & Shared Tasks Papers:
Direct paper submission deadline: April 24, 2023
Notification of acceptance: May 22, 2023
Camera-ready paper due: June 6, 2023
Pre-recorded video due: June 12, 2023
ACL-ClinicalNLP Workshop: July 13-14, 2023, Toronto, Canada
Thanks!
-- ClinicalNLP 2023 Organizers
Anna Rumshisky (UMass Lowell)
Asma Ben Abacha (Microsoft)
Kirk Roberts (University of Texas Health Science Center at Houston)
Steven Bethard (University of Arizona)
Tristan Naumann (Microsoft Research)