Assertion classifier;http://depts.washington.edu/bionlp/liteAssertionClassification_WebRelease_1.0.tgz

This software implements the methodologies for determining the assertion category of a medical concept mentioned in clinical text as described in the following paper:
     Cosmin Adrian Bejan, Lucy Vanderwende, Fei Xia, and Meliha Yetisgen-Yildiz.
     Assertion modeling and its role in clinical phenotype identification.
     Journal of Biomedical Informatics (JBI), 2012.
     http://www.ncbi.nlm.nih.gov/pubmed/23000479
The development of this software was funded by Microsoft Research Connections.


Statistical feature selection http://depts.washington.edu/bionlp/featureRanking_WebRelease_1.0.tgz

This software is part of the statistical feature selection approach used for better identifying patients with a specific phenotype as described in the following paper:
     Cosmin Adrian Bejan, Fei Xia, Lucy Vanderwende, Mark M Wurfel, and Meliha Yetisgen-Yildiz.
     Pneumonia identification using statistical feature selection.
     Journal of the American Medical Informatics Association (JAMIA), 2012.
     http://www.ncbi.nlm.nih.gov/pubmed/22539080
The development of this software was funded by Microsoft Research Connections.


Statistical section chunker http://depts.washington.edu/bionlp/featureRanking_WebRelease_1.0.tgz

The University of Washington - Biomedical Language Processing Group Clinical Record Section Chunker is a Java-based application for identifying section boundaries and labeling section headings for clinical records. It integrates the Mallet Machine Learning toolkit to train a Maximum Entropy model over files annotated with a section heading schema. Details can be found at the following paper:
     M. Tepper, D. Capurro, F. Xia, L. Vanderwende, M. Yetisgen-Yildiz. Statistical Section Segmentation in Free-Text Clinical Records. 
    Proceedings of the International Conference on Language Resources and Evaluation (LREC), Istanbul, May, 2012. 
     
The development of this software was funded by AHRQ.