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Items: 6

1.

The Yale cTAKES extensions for document classification: architecture and application.

Garla V, Lo Re V 3rd, Dorey-Stein Z, Kidwai F, Scotch M, Womack J, Justice A, Brandt C.

J Am Med Inform Assoc. 2011 Sep-Oct;18(5):614-20. doi: 10.1136/amiajnl-2011-000093. Epub 2011 May 27.

2.

Natural Language Processing in Radiology: A Systematic Review.

Pons E, Braun LM, Hunink MG, Kors JA.

Radiology. 2016 May;279(2):329-43. doi: 10.1148/radiol.16142770. Review.

PMID:
27089187
3.

Discerning tumor status from unstructured MRI reports--completeness of information in existing reports and utility of automated natural language processing.

Cheng LT, Zheng J, Savova GK, Erickson BJ.

J Digit Imaging. 2010 Apr;23(2):119-32. doi: 10.1007/s10278-009-9215-7. Epub 2009 May 30. Review.

4.

Natural Language Processing Technologies in Radiology Research and Clinical Applications.

Cai T, Giannopoulos AA, Yu S, Kelil T, Ripley B, Kumamaru KK, Rybicki FJ, Mitsouras D.

Radiographics. 2016 Jan-Feb;36(1):176-91. doi: 10.1148/rg.2016150080. Review.

5.

Temporal data representation, normalization, extraction, and reasoning: A review from clinical domain.

Madkour M, Benhaddou D, Tao C.

Comput Methods Programs Biomed. 2016 May;128:52-68. doi: 10.1016/j.cmpb.2016.02.007. Epub 2016 Feb 23. Review.

6.

Hierarchical semantic structures for medical NLP.

Taira RK, Arnold CW.

Stud Health Technol Inform. 2013;192:1194. Review.

PMID:
23920968

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