Patient-oriented language processing
Monday May 20, 2024
09:00 - 09:05 Opening remarks
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Barbara Di Eugenio, University of Illinois Chicago |
Engaging the Patient in Healthcare: Summarization and Interaction AbstractEffective and compassionate communication with patients is becoming central to healthcare. The talk discusses the results of and lessons learned from three ongoing projects in this space. The first, MyPHA, aims to provide patients with a clear and understandable summary of their hospital stay, which is informed by doctors’ and nurses’ perspectives, and by the strengths and concerns of the patients themselves. The second, VIRTUAL-COACH, models health coaching interactions via text exchanges that encourage patients to adopt specific and realistic physical activity goals. The third, HFChat, envisions an always-on-call conversational assistant for heart failure patients, that they can ask for information about lifestyle issues such as food and exercise.This work is characterized by: large interdisciplinary groups of investigators who bring different perspectives to the research; grounding computational models in ecologically valid data, which is small by its own nature; and the need for culturally valid interventions, since the University of Illinois Health system predominantly serves underprivileged, minority populations. |
Barbara Di Eugenio is a Professor, and Director of Graduate Studies in the department of Computer Science at the University of Illinois Chicago (UIC). Her main area of research is NLP, and its applications to human-computer interaction, educational technology, health care, human-robot interaction, and multimedia systems. She is an NSF CAREER awardee (2002); a UIC University Scholar (2018-2020); a Zenith Award recipient from AWIS, the Association for Women in Science (2022); and an ACL Fellow (Association for Computational Linguistics, 2023). |
Abeed Sarker, Associate Professor and Vice Chair for Research in Biomedical Informatics @ Emory School of Medicine |
Learning and Educating via NLP of Social Media: the Use Case for Substance Use and Overdose in the United StatesSubstance use and overdose is an ongoing crisis in the United States and growing globally. The sphere of substance-related overdose also evolves continuously as novel psychoactive substances enter the supply. Nonmedical substance use surveillance via social media has the potential to provide low-cost and more timely insights than traditional approaches. In our research, we leverage natural language processing (NLP) and machine learning to obtain insights from targeted cohorts of people who use substances about emerging patterns and problems in substance use disorder and treatment. This talk outlines our NLP pipeline for analyzing substance use-related chatter from Twitter (X) and Reddit, and how insights derived from these sources may be used to educate medical practitioners at the forefront of the opioid crisis in the United States, facilitating more patient-centered care. |
Dr. Sarker is an Associate Professor and the Vice Chair for Research at the Department of Biomedical Informatics, School of Medicine, Emory University. He leads several large-scale projects focusing on the application of NLP for health-related tasks, particularly those involving vulnerable populations such as people with substance use disorders, victims of intimate partner violence, and people at risk of self-harm and suicide. His research is primarily funded by the National Institutes of Health (NIH) and Centers for Disease Control and Prevention (CDC). Dr. Sarker’s research has been covered by various national and international media outlets such as the Wall Street Journal, Forbes, and Scripps National News. |
Natalia Grabar, CNRS Researcher, Université de Lille |
Foundations of the Simplification and its Current StateThe purpose of text simplification is to adapt the content of documents in order to make their reading and understanding easier for a given type of population. If the simplification usually aims specific language levels (lexical, morphological, syntactic, semantic...), the available data cannot always provide precise indications required for this process. The talk discusses some sources of such available data. Dr. Grabar also analyzes the current situation related to the exploitation of linguistic indicators during the definition of language complexity and the simplification. |
Dr. Grabar is a CNRS Researcher at the University of Lille. She studied philology at Lviv University, Ukraine and obtained her PhD in Medical Informatics from the Université Paris 6, France. She develops linguistic and statistical methods to access information and knowledge within scientific and technical texts and terminologies. The results are used in information retrieval, information extraction and text simplification. Dr. Grabar has co-authored over 200 publications. |
Graciela Gonzalez-Hernandez, Cedars Sinai Medical Center |
Patients are speaking - are we listening? Incorporating patient perspectives posted online into clinical trialsResearch that aims to be equitable and effective at treating chronic diseases and improving patient outcomes must incorporate a broad range of patient perspectives (health-related uncertainties, beliefs, and experiences). Setting research priorities and designing trials is complex since clinicians, researchers, and patients differ on what is considered important. Patients often prioritize outcomes that directly impact their quality of life, such as symptom relief, functional status, and treatment side effects, while clinicians prioritize outcomes related to survival, disease progression, and biomarker endpoints. Methods commonly used for gaining patient perspectives are often limited are subject to recall and other biases, are expensive and time-consuming, are limited in recruitment number and diversity, and may not comprehensively capture factors important for research design.A vast amount of data from the patient’s perspective is already publicly available: patients openly share useful perspectives on different social media platforms. Despite its potential, approaches for the systematic integration of such data to inform the prioritization and design of health research are still to be developed and validated. In this talk, Prof. Gonzalez-Hernandez discusses her ongoing efforts to enable the extraction of relevant patient perspectives posted online using state-of-the-art natural language processing (NLP) methods, and the promise of their integration into clinical trial design. |
Dr. Gonzalez-Hernandez has over 23 years of experience and more than 200 publications in health AI and NLP, funded by multiple NIH grants. She is currently a Professor and Vice Chair for Research and Education in the Cedars-Sinai Department of Computational Biomedicine. She launched the #SMM4H (Social Media Mining for Health) Workshop and Shared Tasks, which has run annually for the last 8 years. |
March 15, 2024
Workshop Paper Due Date️
March 25, 2024
Notification of acceptance
March 31, 2024
Camera-ready papers due
May 20, 2024
Workshop @ LREC-COLING
Two types of submissions are invited: full papers and short papers.
Full papers should not exceed eight (8) pages of text, plus unlimited references. These are intended to be reports of original research.
Short papers may consist of up to four (4) pages of content, plus unlimited references. Appropriate short paper topics include preliminary results, application notes, descriptions of work in progress, etc.
Electronic Submission:
Submissions must be electronic and in PDF format, using the
Softconf START conference management system. Submissions need to
be anonymous. The submission site is:
https://softconf.com/lrec-coling2024/cl4health2024/
Dual submission policy: papers may NOT be submitted to the workshop if they are or will be concurrently submitted to another meeting or publication.
Main conference resubmissions: We welcome submissions of topically-relevant papers that have been rejected from the main LREC-COLING conference. The scores and reviews from the main conference will be taken into consideration, and the highest ranking papers may be considered without additional review.
This first workshop on patient-oriented language processing aims to establish a general venue for presenting research and applications focused on patients’ needs, including summarizing health records for the patients, answering consumer-health questions using reliable resources, detecting misinformation or potentially harmful information, and providing multi-modal information, such as video, if it better satisfies patients’ needs. Such a venue is needed both to invigorate patient-oriented language processing research and to build a community of researchers interested in this area. The growing interest in this topic is fueled by several current trends:
We invite papers concerning all areas of language processing focused on patients’ health. The workshop will be centered on language technologies for health-related issues concerning the public that include, but are not limited to:
Broadly, CL4Health is concerned with the resources, computational approaches, and behavioral and socio-economic aspects of the public interactions with digital resources in search of health-related information that satisfies their information needs and guides their actions.
The topics of interest for the workshop include but are not limited to the following:
The workshop will be hybrid. Virtual attendees must be registered for the workshop to access the online environment.
Accepted papers will be presented as posters or oral presentations based on the reviewers’ recommendations. All accepted papers will be included in the workshop proceedings and ACL Anthology.