Literature DB >> 10495728

Natural language processing and its future in medicine.

C Friedman1, G Hripcsak.   

Abstract

If accurate clinical information were available electronically, automated applications could be developed to use this information to improve patient care and lower costs. However, to be fully retrievable, clinical information must be structured or coded. Many online patient reports are not coded, but are recorded in natural-language text that cannot be reliably accessed. Natural language processing (NLP) can solve this problem by extracting and structuring text-based clinical information, making clinical data available for use. NLP systems are quite difficult to develop, as they require substantial amounts of knowledge, but progress has definitely been made. Some NLP systems have been developed and tested and have demonstrated promising performance in practical clinical applications; some of these systems have already been deployed. The authors provide background information about NLP, briefly describe some of the systems that have been recently developed, and discuss the future of NLP in medicine.

Entities:  

Mesh:

Year:  1999        PMID: 10495728     DOI: 10.1097/00001888-199908000-00012

Source DB:  PubMed          Journal:  Acad Med        ISSN: 1040-2446            Impact factor:   6.893


  55 in total

1.  Evaluating UMLS strings for natural language processing.

Authors:  A T McCray; O Bodenreider; J D Malley; A C Browne
Journal:  Proc AMIA Symp       Date:  2001

2.  Comparing syntactic complexity in medical and non-medical corpora.

Authors:  D A Campbell; S B Johnson
Journal:  Proc AMIA Symp       Date:  2001

3.  Electronically screening discharge summaries for adverse medical events.

Authors:  Harvey J Murff; Alan J Forster; Josh F Peterson; Julie M Fiskio; Heather L Heiman; David W Bates
Journal:  J Am Med Inform Assoc       Date:  2003-03-28       Impact factor: 4.497

Review 4.  Detecting adverse events using information technology.

Authors:  David W Bates; R Scott Evans; Harvey Murff; Peter D Stetson; Lisa Pizziferri; George Hripcsak
Journal:  J Am Med Inform Assoc       Date:  2003 Mar-Apr       Impact factor: 4.497

5.  Using Medical Text Extraction, Reasoning and Mapping System (MTERMS) to process medication information in outpatient clinical notes.

Authors:  Li Zhou; Joseph M Plasek; Lisa M Mahoney; Neelima Karipineni; Frank Chang; Xuemin Yan; Fenny Chang; Dana Dimaggio; Debora S Goldman; Roberto A Rocha
Journal:  AMIA Annu Symp Proc       Date:  2011-10-22

6.  Leveraging rich annotations to improve learning of medical concepts from clinical free text.

Authors:  Shipeng Yu; Faisal Farooq; Balaji Krishnapuram; Bharat Rao
Journal:  AMIA Annu Symp Proc       Date:  2011-10-22

7.  Development of automated detection of radiology reports citing adrenal findings.

Authors:  Jason J Zopf; Jessica M Langer; William W Boonn; Woojin Kim; Hanna M Zafar
Journal:  J Digit Imaging       Date:  2012-02       Impact factor: 4.056

8.  Laying the groundwork for enterprise-wide medical language processing services: architecture and process.

Authors:  Elizabeth S Chen; Francine L Maloney; Eugene Shilmayster; Howard S Goldberg
Journal:  AMIA Annu Symp Proc       Date:  2009-11-14

9.  Diagnostic code agreement for electronic health records and claims data for tuberculosis.

Authors:  S A Iqbal; C J Isenhour; G Mazurek; B I Truman
Journal:  Int J Tuberc Lung Dis       Date:  2020-07-01       Impact factor: 2.373

10.  Exploring dangerous neighborhoods: latent semantic analysis and computing beyond the bounds of the familiar.

Authors:  Trevor Cohen; Brett Blatter; Vimla Patel
Journal:  AMIA Annu Symp Proc       Date:  2005
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