2nd CFP: Third Workshop on Patient-Oriented Language Processing (CL4Health) @ LREC 2026
Paul Thompson
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 workshop invites papers concerning all areas of language processing focused on patients' health and health-related issues concerning the public. The issues include, but are not limited to, accessibility and trustworthiness of health information provided to the public; explainable and evidence-supported answers to consumer-health questions; accurate summarization of patients' health records at their health literacy level; understanding patients' non-informational needs through their language, and accurate and accessible interpretations of biomedical research. The topics of interest for the workshop include, but are not limited to the following:
- Health-related information needs and online behaviors of the public;
- Quality assurance and ethics considerations in language technologies and approaches applied to text and other modalities for public consumption;
- Summarization of data from electronic health records for patients;
- Detection of misinformation in consumer health-related resources and mitigation of potential harms;
- Consumer health question answering (Community Question Answering)(CQA);
- Biomedical text simplification/adaptation;
- Dialogue systems to support patients' interactions with clinicians, healthcare systems, and online resources;
- Linguistic resources, data, and tools for language technologies focusing on consumer health;
- Infrastructures and pre-trained language models for consumer health;
IMPORTANT DATES (Tentative)
February 18, 2026 -Workshop Paper Due Date️
March 13, 2026 - Notification of acceptance
March 20, 2026 - Camera-ready papers due
April 10, 2026 - Pre-recorded video due (hard deadline)
May 12, 2026 - Workshop
SHARED TASKS
Detecting Dosing Errors from Clinical Trials (CT-DEB'26).
Clinical Trials Dosing Errors Benchmark 2026 is a challenge to predict medication errors in clinical trials using Machine Learning. The Clinical Trials Dosing Errors Benchmark 2026 (CT-DEB'26) is dedicated to automated detection of the risks of medication dosing errors within clinical trial protocols. Leveraging a curated dataset of over 29K trial records derived from the ClinicalTrials.gov registry, participants are challenged to predict the risk probabilities of protocols likely to manifest dosing errors. The dataset consists of various fields with numerical, categorical, as well as textual data types. Once the shared task is concluded and the leaderboard is published, the participants are invited to submit a paper to the CL4Health workshop.
Website: https://www.codabench.org/competitions/11891/
Automatic Case Report Form (CRF) Filling from Clinical Notes.
Case Report Forms (CRFs) are standardized instruments in medical research used to collect patient data in a consistent and reliable way. They consist of a predefined list of items to be filled with patient information. Each item aims to collect a portion of information relevant for a specific clinical goal (e.g., allergies, chronicity of disease, tests results). Automating CRF filling from clinical notes would accelerate clinical research, reduce manual burden on healthcare professionals, and create structured representations that can be directly leveraged to produce accessible, patient- and practitioners-friendly summaries. Even though the healthcare community has been utilizing CRFs as a basic tool in the day-to-day clinical practice, publicly available CRF datasets are scarce, limiting the development of robust NLP systems for this task. We present this Shared Task on CRF-filling aiming to enhance research on systems that can be applied in real clinical settings.
Website: https://sites.google.com/fbk.eu/crf/
ArchEHR-QA 2026: Grounded Question Answering from Electronic Health Records.
The ArchEHR-QA (“Archer”) shared task focuses on answering patients’ health-related questions using their own electronic health records (EHRs). While prior work has explored general health question answering, far less attention has been paid to leveraging patient-specific records and to grounding model outputs in explicit clinical evidence, i.e., linking answers to specific supporting content in the clinical notes. The shared task dataset consists of patient-authored questions, corresponding clinician-interpreted counterparts, clinical note excerpts with sentence-level relevance annotations, and reference clinician-authored answers grounded in the notes. ArchEHR-QA targets the problem of producing answers to patient questions that are supported by and explicitly linked to the underlying clinical notes. This second iteration builds on the 2025 challenge (which was co-located with the ACL 2025 BioNLP Workshop) by expanding the dataset and introducing four complementary subtasks spanning question interpretation, clinical evidence identification, answer generation, and answer–evidence alignment. Teams may participate in any subset of subtasks and will be invited to submit system description papers detailing their approaches and results.
Website: https://archehr-qa.github.io/
FoodBench-QA 2026: Grounded Food & Nutrition Question Answering.
FoodBench-QA 2026 is a shared task challenging systems to answer food and nutrition questions using evidence from nutrient databases and food ontologies.The dataset includes realistic dietary queries, ingredient and their quantities lists, and recipe descriptions, requiring models to perform nutrient estimation, FSA traffic-light prediction, and food entity recognition/linking across three food semantic models. Participants must generate accurate, evidence-based answers across these subtasks (or at least one of it). After the shared task concludes and the leaderboard is released, participants will be invited to submit their work to the Shared Tasks track of the CL4Health workshop at LREC 2026.
Website: https://www.codabench.org/competitions/12112/
LREC provides style files for LaTeX and Microsoft Word at https://lrec2026.info/authors-kit/.
Paul Thompson
Research Fellow
Department of Computer Science
National Centre for Text Mining
Manchester Institute of Biotechnology
University of Manchester
131 Princess Street
Manchester
M1 7DN
UK
http://personalpages.manchester.ac.uk/staff/Paul.Thompson/