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Cited by 688

The DOI matches its Crossref record; 115 of the 115 references it cites were found in a registry or an index.

What can natural language processing do for clinical decision support?

Open access
Demner-Fushman, Dina, Chapman, Wendy W., McDonald, Clement J.·2009·Journal of Biomedical Informatics
DOI 10.1016/j.jbi.2009.08.007
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Abstract

Computerized Clinical Decision Support (CDS) aims to aid decision making of health care providers and the public by providing easily accessible health-related information at the point and time it is needed. Natural Language Processing (NLP) is instrumental in using free-text information to drive CDS, representing clinical knowledge and CDS interventions in standardized formats, and leveraging clinical narrative. The early innovative NLP research of clinical narrative was followed by a period of stable research conducted at the major clinical centers and a shift of mainstream interest to biomedical NLP. This review primarily focuses on the recently renewed interest in development of fundamental NLP methods and advances in the NLP systems for CDS. The current solutions to challenges posed by distinct sublanguages, intended user groups, and support goals are discussed.

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Show all 115 references

Research works printed but not linked: 17

  1. Prokosch HU, McDonald CJ. The effect of computer reminders on the quality of care and resource use. In: Prokosch HU, Dudeck J, editors. Hospital information systems: design and development characteristics; impact and future architecture. Elsevier Science; 1995. p. 221–240.
  2. Wermter, 2004, Really, is medical sublanguage that different? Experimental counter-evidence from tagging medical and newspaper corpora, Stud Health Technol Inform, 107, 560
  3. Pakhomov, 2006, Developing a corpus of clinical notes manually annotated for part-of-speech, Int J Med Inform, 75, 418, 10.1016/j.ijmedinf.2005.08.006 · doi:10.1016/j.ijmedinf.2005.08.006
  4. Ananiadou, 2004, Introduction: named entity recognition in biomedicine, J Biomed Inform, 37, 393, 10.1016/j.jbi.2004.08.011 · doi:10.1016/j.jbi.2004.08.011
  5. Johnson, 1999, A semantic lexicon for medical language processing, J Am Med Inform Assoc, 6, 205, 10.1136/jamia.1999.0060205 · doi:10.1136/jamia.1999.0060205
  6. Deléger L, Namer F, Zweigenbaum P. Morphosemantic parsing of medical compound words: transferring a French analyzer to English. Int J Med Inform 2008. · doi:10.1016/j.ijmedinf.2008.07.016
  7. Mougin, 2006, Using WordNet to improve the mapping of data elements to UMLS for data sources integration, AMIA Annu Symp Proc, 574
  8. Ogren P, Savova G, Chute C. Constructing evaluation corpora for automated clinical named entity recognition. In: Proceedings of the Sixth International Language Resources and Evaluation (LREC’08). Marrakech, Morocco, May 28–30; 2008.
  9. Aramaki E, Miurua Y, Tonoike M, Ohkuma T, Mashuichi H, Ohe Kazuhiko. TEXT2TABLE: medical text summarization system based on named entity recognition and modality identification. In: Proc of the Workshop on BioNLP2009 at the NAACL Symposium; 2009. p. 185–192. · doi:10.3115/1572364.1572390
  10. Cao, 2004, Automatic summarization of patient discharge summaries to create problem lists using medical language processing, Stud Health Technol Inform, 107, 1540
  11. Pakhomov S, Buntrock J, Duffy P. High throughput modularized NLP system for clinical text. In: Proceedings of the 43rd annual meeting on association for computational linguistics (ACL’ 05). Interactive poster and demonstration sessions, June 25–30. Ann Arbor: Michigan; 2005. p. 25–28. · doi:10.3115/1225753.1225760
  12. Downs, 1986, Automated summarization of on-line medical records. In: Proceedings of the fifth world congress on medical lnformatics (Medinfo), Elsevier Science Publishers, 800
  13. Meng, 2005, Automatic generation of repeated patient information for tailoring clinical notes, Int J Med Inform, 74, 663, 10.1016/j.ijmedinf.2005.03.008 · doi:10.1016/j.ijmedinf.2005.03.008
  14. Hardcastle D, Hallett C. Exploring the Use of NLP in the disclosure of electronic patient records. In: ACL’07 workshop on Biological, translational, and clinical language processing (BioNLP’07). Prague, Czech Republic; 2007. p. 161–162. · doi:10.3115/1572392.1572420
  15. Green N. Representing normative arguments in genetic counseling. In: Proceedings of the AAAI-2006 spring symposium on argumentation for consumers of healthcare; 2006 Mar 27–29, Stanford, California; 2006.
  16. Butler, 2007, Internet surveillance. content analysis and monitoring of product-specific internet prescription opioid abuse-related postings, Clin J Pain, 23, 619, 10.1097/AJP.0b013e318125c5cf · doi:10.1097/ajp.0b013e318125c5cf
  17. Malouf R, Davidson B, Sherman A. Mining web texts for brand associations. In: Proceedings of the AAAI-2006 spring symposium on computational approaches to analyzing weblogs, Mar 27–29. Stanford, California; 2006. p. 125–126.

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Metadata

Authors
Demner-Fushman, D., Chapman, W. W., & McDonald, C. J.
Venue
Journal of Biomedical Informatics
Publisher
Elsevier BV
Published
2009-10
Volume
42
Issue
5
Pages
760-772
ISSN
1532-0464 (ISSN-L)
DOI
10.1016/j.jbi.2009.08.007
OpenAlex
W2002514548
Cited by
688
References
115
FWCI
6.12
Type
journal article
Language
en
Open access
bronze
Licence

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CoChat. (2026). Citation check: What can natural language processing do for clinical decision support? https://app.cochat.ai/papers/10.1016/j.jbi.2009.08.007
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Demner-Fushman, D., Chapman, W. W., & McDonald, C. J. (2009). What can natural language processing do for clinical decision support? Journal of Biomedical Informatics. https://doi.org/10.1016/j.jbi.2009.08.007

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