Making Natural Language Processing (NLP) More Accessible for Analysis of Clinical Text

Date: 

Fri May 20, 2011

Host: 

Wendy Chapman
UCSD Division of Biomedical Informatics

Category: 

Natural Language Processing

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ABSTRACT

Researchers in clinical NLP are working towards making NLP more accessible through common data models, shared datasets, and web services. Dr. Chapman will provide a short tutorial on NLP, illustrate its potential use for clinical text, descirbe initiatives leading to more collaborative development, and summarize the vision for an NLP exosystem that was shaped during a recent iDASH workshop. The goal of the ecosystem is to provide an environment for easier development, application, and benchmarking of clinical NLP tools.

 

SPEAKER BIOGRAPHY

After studying linguistics, Dr. Wendy Chapman received her Ph.D in Medical Informatics at the University of Utah with a research focus of natural language processing (NLP). After ten years at the University of Pittsburgh, Dr. Chapman joined UCSD. Her work has mainly addressed extraction of information from clinical reports, including identifying evidence of acute bacterial pneumonia from chest radiography reports and evidence of conditions relevant to detecting disease outbreaks from emergency department reports. She leads the American Medical Informatics Association NLP Working Group and several efforts to develop a collaborative infrastructure for development and application of NLP.

 

 

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