Connection

Stephane Meystre to Natural Language Processing

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This is a "connection" page, showing publications Stephane Meystre has written about Natural Language Processing.
Stephane Meystre
Connection Strength
7.095
Natural Language Processing
  1. Enhancing Comparative Effectiveness Research With Automated Pediatric Pneumonia Detection in a Multi-Institutional Clinical Repository: A PHIS+ Pilot Study. J Med Internet Res. 2017 05 15; 19(5):e162.
    View in: PubMed
    Score: 0.539
  2. Congestive heart failure information extraction framework for automated treatment performance measures assessment. J Am Med Inform Assoc. 2017 Apr 01; 24(e1):e40-e46.
    View in: PubMed
    Score: 0.535
  3. Adapting existing natural language processing resources for cardiovascular risk factors identification in clinical notes. J Biomed Inform. 2015 Dec; 58 Suppl:S128-S132.
    View in: PubMed
    Score: 0.479
  4. Classification of Contextual Use of Left Ventricular Ejection Fraction Assessments. Stud Health Technol Inform. 2015; 216:599-603.
    View in: PubMed
    Score: 0.458
  5. Improving heart failure information extraction by domain adaptation. Stud Health Technol Inform. 2013; 192:185-9.
    View in: PubMed
    Score: 0.398
  6. BoB, a best-of-breed automated text de-identification system for VHA clinical documents. J Am Med Inform Assoc. 2013 Jan 01; 20(1):77-83.
    View in: PubMed
    Score: 0.390
  7. Common data model for natural language processing based on two existing standard information models: CDA+GrAF. J Biomed Inform. 2012 Aug; 45(4):703-10.
    View in: PubMed
    Score: 0.370
  8. Using UMLS lexical resources to disambiguate abbreviations in clinical text. AMIA Annu Symp Proc. 2011; 2011:715-22.
    View in: PubMed
    Score: 0.367
  9. Textractor: a hybrid system for medications and reason for their prescription extraction from clinical text documents. J Am Med Inform Assoc. 2010 Sep-Oct; 17(5):559-62.
    View in: PubMed
    Score: 0.339
  10. A clinical use case to evaluate the i2b2 Hive: predicting asthma exacerbations. AMIA Annu Symp Proc. 2009 Nov 14; 2009:442-6.
    View in: PubMed
    Score: 0.321
  11. Extracting information from textual documents in the electronic health record: a review of recent research. Yearb Med Inform. 2008; 128-44.
    View in: PubMed
    Score: 0.282
  12. Improving the sensitivity of the problem list in an intensive care unit by using natural language processing. AMIA Annu Symp Proc. 2006; 554-8.
    View in: PubMed
    Score: 0.245
  13. Natural language processing to extract medical problems from electronic clinical documents: performance evaluation. J Biomed Inform. 2006 Dec; 39(6):589-99.
    View in: PubMed
    Score: 0.244
  14. Automation of a problem list using natural language processing. BMC Med Inform Decis Mak. 2005 Aug 31; 5:30.
    View in: PubMed
    Score: 0.240
  15. Evaluation of Medical Problem Extraction from Electronic Clinical Documents Using MetaMap Transfer (MMTx). Stud Health Technol Inform. 2005; 116:823-8.
    View in: PubMed
    Score: 0.229
  16. Comparing natural language processing tools to extract medical problems from narrative text. AMIA Annu Symp Proc. 2005; 525-9.
    View in: PubMed
    Score: 0.229
  17. Medical problem and document model for natural language understanding. AMIA Annu Symp Proc. 2003; 455-9.
    View in: PubMed
    Score: 0.199
  18. An artificial intelligence approach to COVID-19 infection risk assessment in virtual visits: A case report. J Am Med Inform Assoc. 2020 08 01; 27(8):1321-1325.
    View in: PubMed
    Score: 0.168
  19. Automatic trial eligibility surveillance based on unstructured clinical data. Int J Med Inform. 2019 09; 129:13-19.
    View in: PubMed
    Score: 0.155
  20. Identifying Falls Risk Screenings Not Documented with Administrative Codes Using Natural Language Processing. AMIA Annu Symp Proc. 2017; 2017:1923-1930.
    View in: PubMed
    Score: 0.144
  21. Heart Failure Medications Detection and Prescription Status Classification in Clinical Narrative Documents. Stud Health Technol Inform. 2015; 216:609-13.
    View in: PubMed
    Score: 0.114
  22. Evaluation of PHI Hunter in Natural Language Processing Research. Perspect Health Inf Manag. 2015; 12:1f.
    View in: PubMed
    Score: 0.114
  23. Can physicians recognize their own patients in de-identified notes? Stud Health Technol Inform. 2014; 205:778-82.
    View in: PubMed
    Score: 0.107
  24. Applying ontological realism to medically unexplained syndromes. Stud Health Technol Inform. 2013; 192:97-101.
    View in: PubMed
    Score: 0.100
  25. Generalizability and comparison of automatic clinical text de-identification methods and resources. AMIA Annu Symp Proc. 2012; 2012:199-208.
    View in: PubMed
    Score: 0.098
  26. Automatically detecting medications and the reason for their prescription in clinical narrative text documents. Stud Health Technol Inform. 2010; 160(Pt 2):944-8.
    View in: PubMed
    Score: 0.081
  27. Inductive creation of an annotation schema and a reference standard for de-identification of VA electronic clinical notes. AMIA Annu Symp Proc. 2009 Nov 14; 2009:416-20.
    View in: PubMed
    Score: 0.080
  28. Randomized controlled trial of an automated problem list with improved sensitivity. Int J Med Inform. 2008 Sep; 77(9):602-12.
    View in: PubMed
    Score: 0.071
Connection Strength

The connection strength for concepts is the sum of the scores for each matching publication.

Publication scores are based on many factors, including how long ago they were written and whether the person is a first or senior author.