Connectionists: BioCreative IX CFP at IJCAI 25: Large Language Models for Clinical and Biomedical NLP

[Salvador Lima]

BioCreative IX Challenge and Workshop CFP

Large Language Models for Clinical and Biomedical NLP at IJCAI

Where, When:

The BioCreative IX workshop
<https://www.ncbi.nlm.nih.gov/research/bionlp/biocreative9> will run with IJCAI
2025 <https://2025.ijcai.org/>, August 16-22, 2025, In Montreal, CA.

BioCreative IX:

The 9th BioCreative workshop seeks to attract researchers interested in
developing and evaluating automatic methods of extracting medically
relevant information from clinical data and aims to bring together the
medical NLP community and the healthcare researchers and practitioners. The
challenge tracks explore MedHopQA, a dataset for benchmarking LLM-based
reasoning systems with disease-centered question answers, ToxHabits, a task
exploring the information extraction related to substance use and abuse in
Spanish clinical content, and Sentence segmentation of real clinical notes
using MIMIC-II clinical notes. We also will feature paper submissions on
relevant topics and poster/tool demonstrations.

Important Dates

March - April: Team Registration

May 12, 2025: Testing predictions, Evaluation results

May 19, 2025: Submission of participants papers deadline

Jun 06, 2025:  Notification of accepted papers deadline

Aug 16- Aug 22 2025: IJCAI 2025



Workshop Proceedings and Special Issue:

The BioCreative IX Proceedings will host all the submissions from
participating teams, and they will be freely available by the time of the
workshop.

In addition, select papers will be invited for a journal BioCreative IX
special issue for work that passes their peer-review process. More details
and information to submit will be posted in June.



Participation:

Teams can participate in one or more of these tracks. Team registration
will continue until April 30th, when final commitment is requested.

To register a team go to the Registration Form
<https://forms.gle/xbQp158cn5pgJ1oj9>. If you have restrictions accessing
Google forms please send e-mail to BiocreativeChallenge at gmail.com.



Call for Papers

We welcome submissions on work that describes research on similar topics to
the three challenges, as well as:

   - Development of benchmarking datasets for clinical NLP
   - Creating and evaluating synthetic data using LLMs and its impact for
   downstream tasks
   - Creative use of data augmentation for increasing tool accuracy and
   trustworthiness
   - Use of LLMs to streamline annotation tasks
   - NLP-systems capable of identifying entities in multilingual corpora
   - NLP-systems capable of semantic interoperability across different
   terminologies/ ontologies for efficient data curation
   - Integrating ontologies and knowledge bases for factual LLM production
   - Annotated corpora and other resources for health care and biomedical
   data modelling

All submissions will be considered for poster presentations and tool
demonstrations at the workshop.



BioCreative IX Tracks:

Track 1: MedHopQA

Large language models (LLMs) are commonly evaluated on their capabilities
to answer questions in various domains, and it has become clear that robust
QA datasets are critical to ensure proper evaluation of LLMs prior to their
deployment in real-world biomedical or healthcare related applications.
This track aims to advance the development of LLM-based systems that are
capable of answering questions that involve multi-step reasoning. We have
created a resource consisting of 1,000 question-answer pairs – focusing on
diseases, genes and chemicals, mostly pertaining to rare diseases – based
on public information in Wikipedia. The participants are encouraged to use
any training data they wish to design and develop their NLP system agents
that understand asserted information on genes, diseases, chemicals etc. and
are able to answer multi-step reasoning questions involving such
information. This track builds on the previous success in biomedical QA
benchmarking (e.g., PubMedQA and BioASQ, MedQA) but differs from them in
the fact that for MedHopQA it is necessary to employ a multi-step reasoning
process to find the correct answer.

Track 2: Sentence segmentation of real-life clinical notes

Sentence segmentation is a fundamental linguistic task and is widely used
as a pre-processing step in many NLP tasks. Although the development of
LLMs and the sparse attention mechanism in transformer networks have
reduced the necessity of sentence level inputs in some NLP tasks, many
models are designed and tested only for shorter sequences. The need for
sentence segmentation is particularly pronounced in clinical notes, as most
clinical NLP tasks depend on this information for annotation and model
training. In this shared task, we challenge participants to detect sentence
boundaries (spans) for MIMIC-III clinical notes, where fragmented and
incomplete sentences, complex graphemic devices (e.g. abbreviations, and
acronyms), and markups are common. To encourage generalizability to
multi-domain texts, participants will receive annotated texts from newswire
articles and biomedical literature, in addition to clinical notes, for
model development and evaluation.

Track 3: ToxHabits

There is a pressing need to extract information related to substance use
and abuse more systematically, including not only smoking and alcohol abuse
but also other harmful drugs and substances from clinical content. These
toxic habits have a considerable health impact on a variety of medical
conditions and also affect the action of prescribed medications. To make
such information actionable, it is critical to not only detect instances of
consumption, but also to characterize certain aspects related to it, such
as duration or mode of administration. Some initial efforts have been made
to automatically detect social determinants of health, including smoking
status, for content in English, but very limited efforts have been made for
content in other languages. Therefore, we propose the ToxHabits track to
address the automatic extraction of substance use and abuse information
from clinical cases in Spanish. This task will consist of three subtasks:
(a) toxic habit mention recognition, (b) detection of relevant clinical
modifiers related to substance abuse, as well as (c) toxic habit condition
QA challenge.

Organizing Committee

   - Dr. Rezarta Islamaj, National Library of Medicine
   - Dr. Graciela Gonzalez-Hernandez, Cedars-Sinai Medical Center
   - Dr. Martin Krallinger, Barcelona Supercomputing Center
   - Dr. Zhiyong Lu, National Library of Medicine



-- 
Salvador Lima Lopez
RESEARCH ENGINEER
Life Sciences - NLP for Biomedical Information Analysis, BSC-CNS
Barcelona, Spain
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mailman.srv.cs.cmu.edu/pipermail/connectionists/attachments/20250403/60158295/attachment.html>