Resources

NegAIT - Free Negation Parser

This negation parser is a java application annotates English Text for Sentential, Morphological, and Double Negation. The input file is a text file, and the output file is in .xml format.

When using this parser, please refer to our paper:

  • P. Mukherjee, G. Leroy, D. Kauchak, S. Rajnarayanan, D. Diaz, N. Yuan, T. Pritchard, and S. Colina, "NegAIT: A New Parser for Medical Text Simplification Using Morphological, Sentential and Double Negation", Journal of Biomedical Informatics, Vol 69, May 2017, Pages 55–62,  2017. [Link to Paper]

After fixing a few issues, performance on the gold standard described in the paper is now:

Performance

This parser was developed as part of research supported by the National Library of Medicine of the National Institutes of Health under Award Number R01LM011975.  The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

 

Machine Learning for Autism Spectrum Disorders

CASE STATUS ASSIGNMENT

We have developed machine learning algorithms to automatically assign autism case status to electronic health records. These algorithms automatically select the relevant terms and features and achieved 86% accuracy. Find the details here: 

G. Leroy, Y. Gu, S. Pettygrove and M. Kurzius-Spencer, "Automated Lexicon and Feature Construction using Word Embedding and Clustering for EHR Classification", 22nd International Conference on Applications of Natural Language to Information Systems, Belgium, June 22-24, 2017.[Link to Paper]

We expect to significantly increase this accuracy in the near future.

 

 

Text Simplification - Lessons Learned

Coming soon ... a summary of all our features tested and validated.

 

 

Presentations

AMCIS 2017 - Doctoral Consortium Panel Presentation - Your job talk.