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Items: 1 to 20 of 104


A system for classifying disease comorbidity status from medical discharge summaries using automated hotspot and negated concept detection.

Ambert KH, Cohen AM.

J Am Med Inform Assoc. 2009 Jul-Aug;16(4):590-5. doi: 10.1197/jamia.M3095. Epub 2009 Apr 23.


Identifying patient smoking status from medical discharge records.

Uzuner O, Goldstein I, Luo Y, Kohane I.

J Am Med Inform Assoc. 2008 Jan-Feb;15(1):14-24. Epub 2007 Oct 18.


Mayo clinic NLP system for patient smoking status identification.

Savova GK, Ogren PV, Duffy PH, Buntrock JD, Chute CG.

J Am Med Inform Assoc. 2008 Jan-Feb;15(1):25-8. Epub 2007 Oct 18.


Use of semantic features to classify patient smoking status.

McCormick PJ, Elhadad N, Stetson PD.

AMIA Annu Symp Proc. 2008 Nov 6:450-4.


Recognizing clinical entities in hospital discharge summaries using Structural Support Vector Machines with word representation features.

Tang B, Cao H, Wu Y, Jiang M, Xu H.

BMC Med Inform Decis Mak. 2013;13 Suppl 1:S1. doi: 10.1186/1472-6947-13-S1-S1. Epub 2013 Apr 5.


Medical i2b2 NLP smoking challenge: the A-Life system architecture and methodology.

Heinze DT, Morsch ML, Potter BC, Sheffer RE Jr.

J Am Med Inform Assoc. 2008 Jan-Feb;15(1):40-3. Epub 2007 Oct 18.


Semantic classification of diseases in discharge summaries using a context-aware rule-based classifier.

Solt I, Tikk D, Gál V, Kardkovács ZT.

J Am Med Inform Assoc. 2009 Jul-Aug;16(4):580-4. doi: 10.1197/jamia.M3087. Epub 2009 Apr 23.


Automatic construction of rule-based ICD-9-CM coding systems.

Farkas R, Szarvas G.

BMC Bioinformatics. 2008 Apr 11;9 Suppl 3:S10. doi: 10.1186/1471-2105-9-S3-S10.


A classification approach to coreference in discharge summaries: 2011 i2b2 challenge.

Xu Y, Liu J, Wu J, Wang Y, Tu Z, Sun JT, Tsujii J, Chang EI.

J Am Med Inform Assoc. 2012 Sep-Oct;19(5):897-905. doi: 10.1136/amiajnl-2011-000734. Epub 2012 Apr 13.


Using implicit information to identify smoking status in smoke-blind medical discharge summaries.

Wicentowski R, Sydes MR.

J Am Med Inform Assoc. 2008 Jan-Feb;15(1):29-31. Epub 2007 Oct 18.


Identifying smokers with a medical extraction system.

Clark C, Good K, Jezierny L, Macpherson M, Wilson B, Chajewska U.

J Am Med Inform Assoc. 2008 Jan-Feb;15(1):36-9. Epub 2007 Oct 18.


Detecting abbreviations in discharge summaries using machine learning methods.

Wu Y, Rosenbloom ST, Denny JC, Miller RA, Mani S, Giuse DA, Xu H.

AMIA Annu Symp Proc. 2011;2011:1541-9. Epub 2011 Oct 22.


Learning regular expressions for clinical text classification.

Bui DD, Zeng-Treitler Q.

J Am Med Inform Assoc. 2014 Sep-Oct;21(5):850-7. doi: 10.1136/amiajnl-2013-002411. Epub 2014 Feb 27.


Automating the assignment of diagnosis codes to patient encounters using example-based and machine learning techniques.

Pakhomov SV, Buntrock JD, Chute CG.

J Am Med Inform Assoc. 2006 Sep-Oct;13(5):516-25. Epub 2006 Jun 23.


Comprehensive temporal information detection from clinical text: medical events, time, and TLINK identification.

Sohn S, Wagholikar KB, Li D, Jonnalagadda SR, Tao C, Komandur Elayavilli R, Liu H.

J Am Med Inform Assoc. 2013 Sep-Oct;20(5):836-42. doi: 10.1136/amiajnl-2013-001622. Epub 2013 Apr 4.


Medical text representations for inductive learning.

Wilcox A, Hripcsak G.

Proc AMIA Symp. 2000:923-7.


High accuracy information extraction of medication information from clinical notes: 2009 i2b2 medication extraction challenge.

Patrick J, Li M.

J Am Med Inform Assoc. 2010 Sep-Oct;17(5):524-7. doi: 10.1136/jamia.2010.003939.


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.


From episodes of care to diagnosis codes: automatic text categorization for medico-economic encoding.

Ruch P, Gobeilla J, Tbahritia I, Geissbühlera A.

AMIA Annu Symp Proc. 2008 Nov 6:636-40.

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