Connection

DAVID BATES to Natural Language Processing

This is a "connection" page, showing publications DAVID BATES has written about Natural Language Processing.
Connection Strength

0.668
  1. Performance and improvement strategies for adapting generative large language models for electronic health record applications: A systematic review. Int J Med Inform. 2026 Jan; 205:106091.
    View in: PubMed
    Score: 0.224
  2. A Concept-Wide Association Study of Clinical Notes to Discover New Predictors of Kidney Failure. Clin J Am Soc Nephrol. 2016 12 07; 11(12):2150-2158.
    View in: PubMed
    Score: 0.121
  3. Enhancing early detection of cognitive decline in the elderly: a comparative study utilizing large language models in clinical notes. EBioMedicine. 2024 Nov; 109:105401.
    View in: PubMed
    Score: 0.053
  4. Detecting adverse events using information technology. J Am Med Inform Assoc. 2003 Mar-Apr; 10(2):115-28.
    View in: PubMed
    Score: 0.047
  5. Intelligent Telehealth in Pharmacovigilance: A Future Perspective. Drug Saf. 2022 05; 45(5):449-458.
    View in: PubMed
    Score: 0.045
  6. Examination of Stigmatizing Language in the Electronic Health Record. JAMA Netw Open. 2022 01 04; 5(1):e2144967.
    View in: PubMed
    Score: 0.043
  7. PASCLex: A comprehensive post-acute sequelae of COVID-19 (PASC) symptom lexicon derived from electronic health record clinical notes. J Biomed Inform. 2022 01; 125:103951.
    View in: PubMed
    Score: 0.043
  8. A value set for documenting adverse reactions in electronic health records. J Am Med Inform Assoc. 2018 06 01; 25(6):661-669.
    View in: PubMed
    Score: 0.034
  9. Use of electronic healthcare records to identify complex patients with atrial fibrillation for targeted intervention. J Am Med Inform Assoc. 2017 Mar 01; 24(2):339-344.
    View in: PubMed
    Score: 0.031
  10. Food entries in a large allergy data repository. J Am Med Inform Assoc. 2016 Apr; 23(e1):e79-87.
    View in: PubMed
    Score: 0.028
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.