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Treffer: The Sublanguage of Clinical Problem Lists: A Corpus Analysis.

Title:
The Sublanguage of Clinical Problem Lists: A Corpus Analysis.
Authors:
Peterson KJ; Division of Information Management and Analytics, Mayo Clinic, Rochester, MN.; Bioinformatics and Computational Biology Program, University of Minnesota, Minneapolis, MN., Liu H; Department of Health Sciences Research, Mayo Clinic, Rochester, MN.
Source:
AMIA ... Annual Symposium proceedings. AMIA Symposium [AMIA Annu Symp Proc] 2018 Dec 05; Vol. 2018, pp. 1451-1460. Date of Electronic Publication: 2018 Dec 05 (Print Publication: 2018).
Publication Type:
Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't
Language:
English
Journal Info:
Publisher: American Medical Informatics Association Country of Publication: United States NLM ID: 101209213 Publication Model: eCollection Cited Medium: Internet ISSN: 1942-597X (Electronic) Linking ISSN: 15594076 NLM ISO Abbreviation: AMIA Annu Symp Proc Subsets: MEDLINE
Imprint Name(s):
Original Publication: Bethesda, MD : American Medical Informatics Association, c2003-
MeSH Terms:
Medical Records, Problem-Oriented* , Natural Language Processing* , Systematized Nomenclature of Medicine* , Terminology as Topic*, Humans ; Semantics ; Unified Medical Language System
References:
Mayo Clin Proc. 2006 Jun;81(6):741-8. (PMID: 16770974)
Proc AMIA Symp. 2001;:662-6. (PMID: 11825268)
AMIA Jt Summits Transl Sci Proc. 2012;2012:30-7. (PMID: 22779045)
N Engl J Med. 1968 Mar 14;278(11):593-600. (PMID: 5637758)
BMC Med Inform Decis Mak. 2011 May 25;11:36. (PMID: 21612639)
J Am Med Inform Assoc. 1999 May-Jun;6(3):205-18. (PMID: 10332654)
Methods Inf Med. 1995 Mar;34(1-2):176-86. (PMID: 9082129)
AMIA Annu Symp Proc. 2008 Nov 06;:753-7. (PMID: 18999284)
J Biomed Inform. 2002 Aug;35(4):222-35. (PMID: 12755517)
Proc AMIA Symp. 2001;:17-21. (PMID: 11825149)
AMIA Annu Symp Proc. 2012;2012:568-76. (PMID: 23304329)
Nucleic Acids Res. 2004 Jan 1;32(Database issue):D267-70. (PMID: 14681409)
Nucleic Acids Res. 2011 Jul;39(Web Server issue):W541-5. (PMID: 21672956)
Yearb Med Inform. 2008;:67-79. (PMID: 18660879)
Health Inf Sci Syst. 2014 Feb 07;2:3. (PMID: 25825667)
J Am Med Inform Assoc. 2011 Sep-Oct;18(5):601-6. (PMID: 21508414)
J Am Med Inform Assoc. 1994 Mar-Apr;1(2):142-60. (PMID: 7719796)
AMIA Annu Symp Proc. 2003;:699-703. (PMID: 14728263)
J Biomed Inform. 2002 Aug;35(4):215-21. (PMID: 12755516)
J Am Med Inform Assoc. 2018 Mar 1;25(3):353-359. (PMID: 29202185)
J Am Med Inform Assoc. 2011 Mar-Apr;18(2):181-6. (PMID: 21233086)
Int J Med Inform. 2003 Sep;71(2-3):89-102. (PMID: 14519402)
J Am Med Inform Assoc. 2009 May-Jun;16(3):362-70. (PMID: 19261947)
Stud Health Technol Inform. 2007;129(Pt 1):679-83. (PMID: 17911803)
J Am Med Inform Assoc. 2008 Nov-Dec;15(6):744-51. (PMID: 18755993)
J Biomed Semantics. 2017 Sep 11;8(1):37. (PMID: 28893314)
Grant Information:
R01 EB019403 United States EB NIBIB NIH HHS; U01 TR002062 United States TR NCATS NIH HHS
Entry Date(s):
Date Created: 20190301 Date Completed: 20200127 Latest Revision: 20231005
Update Code:
20250114
PubMed Central ID:
PMC6371258
PMID:
30815190
Database:
MEDLINE

Weitere Informationen

.Summary-level clinical text is an important part of the overall clinical record as it provides a condensed and efficient view into the issues pertinent to the patient, or their "problem list." These problem lists contain a wealth of information pertaining to the patient's history as well as current state and well-being. In this study, we explore the structure of these problem list entries both grammatically and semantically in an attempt to learn the specialized rules, or "sublanguage" that governs them. Our methods focus on a large-scale corpus analysis of problem list entries. Using Resource Description Framework (RDF), we incorporate inferencing and reasoning via domain-specific ontologies into our analysis to elicit common semantic patterns. We also explore how these methods can be applied dynamically to learn specific sublanguage features of interest for a particular concept or topic within the domain.