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Connection

Brett Beaulieu-Jones to Electronic Health Records

This is a "connection" page, showing publications Brett Beaulieu-Jones has written about Electronic Health Records.
  1. MISSING DATA IMPUTATION IN THE ELECTRONIC HEALTH RECORD USING DEEPLY LEARNED AUTOENCODERS. Pac Symp Biocomput. 2017; 22:207-218.
    View in: PubMed
    Score: 0.436
  2. Semi-supervised learning of the electronic health record for phenotype stratification. J Biomed Inform. 2016 12; 64:168-178.
    View in: PubMed
    Score: 0.429
  3. Predicting seizure recurrence after an initial seizure-like episode from routine clinical notes using large language models: a retrospective cohort study. Lancet Digit Health. 2023 12; 5(12):e882-e894.
    View in: PubMed
    Score: 0.176
  4. Generate Analysis-Ready Data for Real-world Evidence: Tutorial for Harnessing Electronic Health Records With Advanced Informatic Technologies. J Med Internet Res. 2023 05 25; 25:e45662.
    View in: PubMed
    Score: 0.170
  5. Validation of an internationally derived patient severity phenotype to support COVID-19 analytics from electronic health record data. J Am Med Inform Assoc. 2021 07 14; 28(7):1411-1420.
    View in: PubMed
    Score: 0.149
  6. What Every Reader Should Know About Studies Using Electronic Health Record Data but May Be Afraid to Ask. J Med Internet Res. 2021 03 02; 23(3):e22219.
    View in: PubMed
    Score: 0.145
  7. Mapping Patient Trajectories using Longitudinal Extraction and Deep Learning in the MIMIC-III Critical Care Database. Pac Symp Biocomput. 2018; 23:123-132.
    View in: PubMed
    Score: 0.117
  8. Temporal Trends in Clinical Evidence of 5-Year Survival Within Electronic Health Records Among Patients With Early-Stage Colon Cancer Managed With Laparoscopy-Assisted Colectomy vs Open Colectomy. JAMA Netw Open. 2022 06 01; 5(6):e2218371.
    View in: PubMed
    Score: 0.040
  9. Opportunities and obstacles for deep learning in biology and medicine. J R Soc Interface. 2018 04; 15(141).
    View in: PubMed
    Score: 0.030
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.