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Items: 1 to 20 of 111

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.

MITRE system for clinical assertion status classification.

Clark C, Aberdeen J, Coarr M, Tresner-Kirsch D, Wellner B, Yeh A, Hirschman L.

J Am Med Inform Assoc. 2011 Sep-Oct;18(5):563-7. doi: 10.1136/amiajnl-2011-000164. Epub 2011 Apr 22.

3.

Information extraction from multi-institutional radiology reports.

Hassanpour S, Langlotz CP.

Artif Intell Med. 2016 Jan;66:29-39. doi: 10.1016/j.artmed.2015.09.007. Epub 2015 Oct 3.

4.

A study of machine-learning-based approaches to extract clinical entities and their assertions from discharge summaries.

Jiang M, Chen Y, Liu M, Rosenbloom ST, Mani S, Denny JC, Xu H.

J Am Med Inform Assoc. 2011 Sep-Oct;18(5):601-6. doi: 10.1136/amiajnl-2011-000163. Epub 2011 Apr 20.

5.

A knowledge discovery and reuse pipeline for information extraction in clinical notes.

Patrick JD, Nguyen DH, Wang Y, Li M.

J Am Med Inform Assoc. 2011 Sep-Oct;18(5):574-9. doi: 10.1136/amiajnl-2011-000302. Epub 2011 Jul 7.

6.

2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text.

Uzuner Ö, South BR, Shen S, DuVall SL.

J Am Med Inform Assoc. 2011 Sep-Oct;18(5):552-6. doi: 10.1136/amiajnl-2011-000203. Epub 2011 Jun 16.

7.

A flexible framework for deriving assertions from electronic medical records.

Roberts K, Harabagiu SM.

J Am Med Inform Assoc. 2011 Sep-Oct;18(5):568-73. doi: 10.1136/amiajnl-2011-000152. Epub 2011 Jul 1.

8.

Knowledge-based biomedical word sense disambiguation: an evaluation and application to clinical document classification.

Garla VN, Brandt C.

J Am Med Inform Assoc. 2013 Sep-Oct;20(5):882-6. doi: 10.1136/amiajnl-2012-001350. Epub 2012 Oct 16. Erratum in: J Am Med Inform Assoc. 2014 May-Jun;21(3):568.

9.

Ontology-guided feature engineering for clinical text classification.

Garla VN, Brandt C.

J Biomed Inform. 2012 Oct;45(5):992-8. doi: 10.1016/j.jbi.2012.04.010. Epub 2012 May 9.

10.

Automated concept-level information extraction to reduce the need for custom software and rules development.

D'Avolio LW, Nguyen TM, Goryachev S, Fiore LD.

J Am Med Inform Assoc. 2011 Sep-Oct;18(5):607-13. doi: 10.1136/amiajnl-2011-000183. Epub 2011 Jun 22.

11.

Automatic identification of critical follow-up recommendation sentences in radiology reports.

Yetisgen-Yildiz M, Gunn ML, Xia F, Payne TH.

AMIA Annu Symp Proc. 2011;2011:1593-602. Epub 2011 Oct 22.

12.

Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications.

Savova GK, Masanz JJ, Ogren PV, Zheng J, Sohn S, Kipper-Schuler KC, Chute CG.

J Am Med Inform Assoc. 2010 Sep-Oct;17(5):507-13. doi: 10.1136/jamia.2009.001560.

13.

A study of transportability of an existing smoking status detection module across institutions.

Liu M, Shah A, Jiang M, Peterson NB, Dai Q, Aldrich MC, Chen Q, Bowton EA, Liu H, Denny JC, Xu H.

AMIA Annu Symp Proc. 2012;2012:577-86. Epub 2012 Nov 3.

14.

Machine-learned solutions for three stages of clinical information extraction: the state of the art at i2b2 2010.

de Bruijn B, Cherry C, Kiritchenko S, Martin J, Zhu X.

J Am Med Inform Assoc. 2011 Sep-Oct;18(5):557-62. doi: 10.1136/amiajnl-2011-000150. Epub 2011 May 12.

15.

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
16.

Automated outcome classification of emergency department computed tomography imaging reports.

Yadav K, Sarioglu E, Smith M, Choi HA.

Acad Emerg Med. 2013 Aug;20(8):848-54. doi: 10.1111/acem.12174.

17.

Performance of a Machine Learning Classifier of Knee MRI Reports in Two Large Academic Radiology Practices: A Tool to Estimate Diagnostic Yield.

Hassanpour S, Langlotz CP, Amrhein TJ, Befera NT, Lungren MP.

AJR Am J Roentgenol. 2017 Apr;208(4):750-753. doi: 10.2214/AJR.16.16128. Epub 2017 Jan 31.

PMID:
28140627
18.

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.

19.

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.

20.

Building a common pipeline for rule-based document classification.

Patterson OV, Ginter T, DuVall SL.

Stud Health Technol Inform. 2013;192:1211.

PMID:
23920985

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