Temporal bone radiology report classification using open source machine learning and natural langue processing libraries-Reference-Cited by-同舟云学术

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Temporal bone radiology report classification using open source machine learning and natural langue processing libraries

Published:2016-06-06 Issue:1 Volume:16 Page:
ISSN:1472-6947
Container-title:BMC Medical Informatics and Decision Making
language:en
Short-container-title:BMC Med Inform Decis Mak

Author:

Masino Aaron J.,Grundmeier Robert W.,Pennington Jeffrey W.,Germiller John A.,Crenshaw E. Bryan

Funder

NIH National Institutes of Deafness and Other Communication Disorders

Publisher

Springer Science and Business Media LLC

Subject

Health Informatics,Health Policy,Computer Science Applications

Link

http://link.springer.com/content/pdf/10.1186/s12911-016-0306-3.pdf

Reference35 articles.

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2. Hersh WR, Weiner MG, Embi PJ, et al. Caveats for the Use of Operational Electronic Health Record Data in Comparative Effectiveness Research. Med Care. 2013. doi: 10.1097/MLR.0b013e31829b1dbd .

3. Newton KM, Peissig PL, Kho AN, Bielinski SJ, Berg RL, Choudhary V, et al. Validation of electronic medical record-based phenotyping algorithms: results and lessons learned from the eMERGE network. J Am Med Inform Assoc. 2013;20(e1):e147–54.

4. Wang X, Hripcsak G, Markatou M, Friedman C. Active computerized pharmacovigilance using natural language processing, statistics, and electronic health records: a feasibility study. J Am Med Inform Assoc. 2009;16(3):328–37.

5. Lependu P, Iyer SV, Fairon C, Shah NH. Annotation analysis for testing drug safety signals using unstructured clinical notes. J Biomed Semantics. 2012; doi: 10.1186/2041-1480-3-S1-S5 .

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