posted by user: grupocole || 3580 views || tracked by 12 users: [display]

LUCT 2011 : ICML 2011 Workshop on Learning from Unstructured Clinical Text


When Jul 2, 2011 - Jul 2, 2011
Where Washington, USA
Submission Deadline Apr 29, 2011
Notification Due May 20, 2011
Final Version Due Jun 15, 2011
Categories    MLP

Call For Papers

ICML 2011 Workshop on Learning from Unstructured Clinical Text


ICML 2011 Workshop on Learning from Unstructured Clinical Text

The rapid growth of information technology promises to change the practice
of medicine as we know it. Large volumes of clinical data are now
digitized as part of routine patient care, and clinical decisions are made
more accurately and more efficiently than ever before with the growing
prevalence of Electronic Medical Record (EMR) systems. Despite the
increasing emphasis on collecting information in structured fields of
EMRs, much of the key information needed for measuring and driving process
efficiencies still resides in unstructured (free) text. This information
often needs to be mined and extracted into a structured form.

The purpose of this multi-disciplinary workshop is to bring together
researchers from machine learning, computational linguistics, and medical
informatics researchers who share an interest in problems and applications
of learning from unstructured clinical text. The goal of this workshop
will be to bridge the gap between the theory of machine learning, natural
language processing, and the applications and needs of the healthcare

Call for Papers
The workshop seeks high quality, original and unpublished work on
algorithms, theory and systems for learning from unstructured clinical

Topics of Interest

This workshop would like to encourage submissions on any of (but not
limited to) the following topics:


* Information Extraction and Retrieval from Clinical Text
* Question Answering from Clinical Text
* Clinical Ontologies
* Patient Identification with Unstructured Clinical Text
* Patient risk assessment from Unstructured Clinical Text
* Combining Phenotype and Genotype Data


* Learning from noisy text data
* Learning from multiple annotators
* Learning with data not missing at random
* Active Learning to reduce expert annotation costs
* Combining Unstructured and Structured Text for inference


We call for paper contribution of up to 8 pages to the workshop using ICML
style. The accepted papers will be available for downloading from the
workshop website's Proceedings page. Accepted papers will be either
presented as a talk or poster. Papers should be emailed to the organizers
at Please indicate your
preference for oral or poster presentation.

Publication of Papers

Accepted papers will be either presented as a talk or poster. They will
also be available in an online proceedings that will be made available
prior to the workshop. Extended versions of some accepted papers will also
be invited for inclusion in an edited book on the same topic as the

Important Dates
Please remember the following dates in relation to paper presentations.

April 29 - Deadline of submission
May 20 - Notification of Acceptance
June 15 - Camera Ready Submission
July 2 - Workshop Proper

Read more:

Related Resources

ICML 2020   37th International Conference on Machine Learning
KDF 2021   The AAAI-21 Workshop on Knowledge Discovery from Unstructured Data in Financial Services
EMNLP 2020   Conference on Empirical Methods in Natural Language Processing
ADRW 2021   «Archives during rebellions and wars». From the age of Napoleon to the cyber war era
CodiEsp (eHealth CLEF 2020) 2020   CLEF-2020 CodiEsp: clinical text classification, indexing and explainable AI Task (eHealth CLEF 2020)
TSD 2020   Twenty-third International Conference on Text, Speech and Dialogue
AIME 2021   Artificial Intelligence in Medicine in Europe
FL-ICML 2020   International Workshop on Federated Learning for User Privacy and Data Confidentiality in Conjunction with ICML 2020
ICML WS: Optimization for ML 2020   ICML 2020 Workshop: Beyond First Order Methods in Machine Learning
SI-DAMLE 2020   Special Issue on Data Analytics and Machine Learning in Education