36.3734, Confs: Workshop at LREC 2026: Patient-Oriented Language Processing (Spain)

[The LINGUIST List]

LINGUIST List: Vol-36-3734. Thu Dec 04 2025. ISSN: 1069 - 4875.

Subject: 36.3734, Confs: Workshop at LREC 2026: Patient-Oriented Language Processing (Spain)

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Date: 04-Dec-2025
From: Paul Thompson [paul.thompson at manchester.ac.uk]
Subject: Workshop at LREC 2026: Patient-Oriented Language Processing


Workshop at LREC 2026: Patient-Oriented Language Processing
Short Title: CL4Health 2026

Date: 16-May-2026 - 16-May-2026
Location: Palma, Mallorca, Spain
Contact: Paul Thompson
Contact Email: paul.thompson at manchester.ac.uk
Meeting URL: https://bionlp.nlm.nih.gov/cl4health2026/

Linguistic Field(s): Computational Linguistics

Submission Deadline: 13-Mar-2026

Scope:
CL4Health fills the gap among the different biomedical language
processing workshops by providing a general venue for a broad spectrum
of patient-oriented language processing research. The third workshop
on patient-oriented language processing follows the successful
CL4Health workshops (co-located with LREC-COLING 2024 and NAACL 2025),
which clearly demonstrated the need for a computational linguistics
venue focused on language related to public health.
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 16, 2026 - Workshop
Shared Tasks:
Detecting Dosing Errors from Clinical Trials:
Medication errors constitute a significant threat to public health
worldwide. Although various types of errors may occur, dosing errors
have been identified as one of the most frequent types. The objective
of the shared task is to develop and evaluate machine learning methods
capable of analyzing clinical trial data (including structured
metadata and free-text protocol descriptions) to identify trials that
are likely to experience unusually high rates of dosing errors. Such
predictive tools could serve as early-warning systems, supporting more
reliable trial design and enhancing medication safety. A
human-annotated dataset comprising 40,000 clinical trials will be used
for the training and validation set. Submissions will be evaluated
primarily using the F1-score, with AUROC and AUPRC reported as
complementary metrics. To avoid participants using unauthorized data
for training, only submissions of fully reproducible, open methods
will be considered.
Automatic Case Report Form (CRF) Filling from Clinical Notes:
Case Report Forms are standardized instruments in medical research
used to collect patient data consistently and reliably. They consist
of predefined items to be filled with patient information. 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-friendly, and practitioner-friendly summaries. The
shared task focuses on developing systems that take clinical
narratives as input and automatically populate the relevant slots in a
CRF. Two different (synthetic and real clinical data) multilingual
datasets covering English and Italian will be shared with the
participants to develop the system. The evaluation will be performed
in terms of F1-score by comparing the system's outputs with ground
truth labels.
Grounded Question Answering from Electronic Health Records:
While there have been studies on answering general health-related
queries, few have focused on their own medical records. Furthermore,
grounding (linking responses to specific evidence) is critical in
medicine. Yet, despite extensive studies in open domains, its
application in the clinical domain remains underexplored. To foster
research in these sparsely studied areas of clinical natural language
processing, the ArchEHR-QA (“Archer”) shared task was introduced as
part of the BioNLP Workshop at ACL 2025. Given a patient-posed natural
language question, the corresponding clinician-interpreted question,
and the patient's clinical note excerpt, the task is to produce a
natural language answer with citations to the specific note sentences.
The ArchEHR-QA dataset is based on real-life patients' questions from
public health forums aligned with clinical notes from publicly
accessible EHR databases (MIMIC-III/IV) to form a cohesive
question-answer source case. Submissions will be evaluated for
evidence use (“Factuality”) and answer quality (“Relevance”).
Factuality is measured via Precision, Recall, and F1 Scores between
the cited evidence sentences in systems' answers and ground truth
labels. Relevance is measured against ground truth answers using BLEU,
ROUGE, SARI, BERTScore, AlignScore, and MEDCON.
Submissions:
Two types of submissions are invited:
- 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.
Papers should follow LREC 2026 formatting.
LREC provides style files for LaTeX and Microsoft Word at
https://lrec2026.info/authors-kit/.
Submission site: https://softconf.com/lrec2026/CL4Health/
Dual submission policy: papers may NOT be submitted to the workshop if
they are or will be concurrently submitted to another meeting or
publication.
Share your LRs: When submitting a paper from the START page, authors
will be asked to provide essential information about resources (in a
broad sense, i.e. also technologies, standards, evaluation kits, etc.)
that have been used for the work described in the paper or are a new
result of your research. Moreover, ELRA encourages all LREC authors to
share the described LRs (data, tools, services, etc.) to enable their
reuse and replicability of experiments (including evaluation ones).
Meeting:
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.
Organizers:
- Deepak Gupta, US National Library of Medicine
- Paul Thompson, National Centre for Text Mining and University of
Manchester, UK
- Dina Demner-Fushman, US National Library of Medicine
- Sophia Ananiadou, National Centre for Text Mining and University of
Manchester, UK



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