Connection

Stephane Meystre to Electronic Health Records

Back to Details
This is a "connection" page, showing publications Stephane Meystre has written about Electronic Health Records.
Stephane Meystre
Connection Strength
3.604
Electronic Health Records
  1. 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: 0.378
  2. Classification of Contextual Use of Left Ventricular Ejection Fraction Assessments. Stud Health Technol Inform. 2015; 216:599-603.
    View in: PubMed
    Score: 0.361
  3. Heart Failure Medications Detection and Prescription Status Classification in Clinical Narrative Documents. Stud Health Technol Inform. 2015; 216:609-13.
    View in: PubMed
    Score: 0.361
  4. Text de-identification for privacy protection: a study of its impact on clinical text information content. J Biomed Inform. 2014 Aug; 50:142-50.
    View in: PubMed
    Score: 0.339
  5. Generalizability and comparison of automatic clinical text de-identification methods and resources. AMIA Annu Symp Proc. 2012; 2012:199-208.
    View in: PubMed
    Score: 0.311
  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: 0.308
  7. Evaluating current automatic de-identification methods with Veteran's health administration clinical documents. BMC Med Res Methodol. 2012 Jul 27; 12:109.
    View in: PubMed
    Score: 0.305
  8. 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: 0.268
  9. Automatic de-identification of textual documents in the electronic health record: a review of recent research. BMC Med Res Methodol. 2010 Aug 02; 10:70.
    View in: PubMed
    Score: 0.266
  10. Impact of De-Identification on Clinical Text Classification Using Traditional and Deep Learning Classifiers. Stud Health Technol Inform. 2019 Aug 21; 264:283-287.
    View in: PubMed
    Score: 0.125
  11. 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: 0.113
  12. 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: 0.106
  13. Evaluation of PHI Hunter in Natural Language Processing Research. Perspect Health Inf Manag. 2015; 12:1f.
    View in: PubMed
    Score: 0.090
  14. Evaluating the effects of machine pre-annotation and an interactive annotation interface on manual de-identification of clinical text. J Biomed Inform. 2014 Aug; 50:162-72.
    View in: PubMed
    Score: 0.087
  15. 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: 0.073
  16. 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: 0.064
  17. 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: 0.033
  18. Evaluating the informatics for integrating biology and the bedside system for clinical research. BMC Med Res Methodol. 2009 Oct 28; 9:70.
    View in: PubMed
    Score: 0.016
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.