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 community.

Proceedings and Program now available.

Guidelines for Oral Papers: Plan for a 20 minute presentation and 5 minute Q and A (Total of 25 minutes). It is recommended that all oral presenters also bring posters (or printed color slides) for more interaction.

Guidelines for Posters: The maximum size of your poster should be 4 ft x 4 ft. Please set up posters first thing in the morning.

Date: July 2, 2011
Venue: Grand K Room, Hyatt Regency Bellevue, Washington, USA

For any information, please contact learn.clinical.freetext.icml11@gmail.com