Sort by
Items per page

Send to

Choose Destination

Links from PubMed

Items: 1 to 20 of 109


Development and evaluation of RapTAT: a machine learning system for concept mapping of phrases from medical narratives.

Gobbel GT, Reeves R, Jayaramaraja S, Giuse D, Speroff T, Brown SH, Elkin PL, Matheny ME.

J Biomed Inform. 2014 Apr;48:54-65. doi: 10.1016/j.jbi.2013.11.008. Epub 2013 Dec 4.


A semi-automatic semantic method for mapping SNOMED CT concepts to VCM Icons.

Lamy JB, Tsopra R, Venot A, Duclos C.

Stud Health Technol Inform. 2013;192:42-6.


iSMART: Ontology-based Semantic query of CDA documents.

Liu S, Ni Y, Mei J, Li H, Xie G, Hu G, Liu H, Hou X, Pan Y.

AMIA Annu Symp Proc. 2009 Nov 14;2009:375-9.


Validating an ontology-based algorithm to identify patients with type 2 diabetes mellitus in electronic health records.

Rahimi A, Liaw ST, Taggart J, Ray P, Yu H.

Int J Med Inform. 2014 Oct;83(10):768-78. doi: 10.1016/j.ijmedinf.2014.06.002. Epub 2014 Jun 20.


An ontology-based similarity measure for biomedical data-application to radiology reports.

Mabotuwana T, Lee MC, Cohen-Solal EV.

J Biomed Inform. 2013 Oct;46(5):857-68. doi: 10.1016/j.jbi.2013.06.013. Epub 2013 Jul 11.


Semantic Krippendorff's α for measuring inter-rater agreement in SNOMED CT coding studies.

Karlsson D, Gøeg KR, Örman H, Højen AR.

Stud Health Technol Inform. 2014;205:151-5.


Semantic interoperation and electronic health records: context sensitive mapping from SNOMED CT to ICD-10.

Campbell JR, Brear H, Scichilone R, White S, Giannangelo K, Carlsen B, Solbrig H, Fung KW.

Stud Health Technol Inform. 2013;192:603-7.


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.


Detecting Underspecification in SNOMED CT concept definitions through natural language processing.

Pacheco E, Stenzhorn H, Nohama P, Paetzold J, Schulz S.

AMIA Annu Symp Proc. 2009 Nov 14;2009:492-6.


Comparison of ontology-based semantic-similarity measures.

Lee WN, Shah N, Sundlass K, Musen M.

AMIA Annu Symp Proc. 2008 Nov 6:384-8.


Personalized Guideline-Based Treatment Recommendations Using Natural Language Processing Techniques.

Becker M, Böckmann B.

Stud Health Technol Inform. 2017;235:271-275.


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.


Text de-identification for privacy protection: a study of its impact on clinical text information content.

Meystre SM, Ferrández Ó, Friedlin FJ, South BR, Shen S, Samore MH.

J Biomed Inform. 2014 Aug;50:142-50. doi: 10.1016/j.jbi.2014.01.011. Epub 2014 Feb 3.


Implementing reusable software components for SNOMED CT diagram and expression concept representations.

Bánfai B, Porció R, Kovács T.

Stud Health Technol Inform. 2014;205:1028-32.


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.


Identifying synonymy between SNOMED clinical terms of varying length using distributional analysis of electronic health records.

Henriksson A, Conway M, Duneld M, Chapman WW.

AMIA Annu Symp Proc. 2013 Nov 16;2013:600-9. eCollection 2013.


A comprehensive study of named entity recognition in Chinese clinical text.

Lei J, Tang B, Lu X, Gao K, Jiang M, Xu H.

J Am Med Inform Assoc. 2014 Sep-Oct;21(5):808-14. doi: 10.1136/amiajnl-2013-002381. Epub 2013 Dec 17.


A hybrid knowledge-based and data-driven approach to identifying semantically similar concepts.

Pivovarov R, Elhadad N.

J Biomed Inform. 2012 Jun;45(3):471-81. doi: 10.1016/j.jbi.2012.01.002. Epub 2012 Jan 25.

Supplemental Content

Support Center