Published on in Vol 23, No 11 (2021): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/28946, first published .
Using Artificial Intelligence With Natural Language Processing to Combine Electronic Health Record’s Structured and Free Text Data to Identify Nonvalvular Atrial Fibrillation to Decrease Strokes and Death: Evaluation and Case-Control Study

Using Artificial Intelligence With Natural Language Processing to Combine Electronic Health Record’s Structured and Free Text Data to Identify Nonvalvular Atrial Fibrillation to Decrease Strokes and Death: Evaluation and Case-Control Study

Using Artificial Intelligence With Natural Language Processing to Combine Electronic Health Record’s Structured and Free Text Data to Identify Nonvalvular Atrial Fibrillation to Decrease Strokes and Death: Evaluation and Case-Control Study

Peter L Elkin   1, 2, 3 , MD ;   Sarah Mullin   1 , PHO ;   Jack Mardekian   4 , PhD ;   Christopher Crowner   1 , MSc ;   Sylvester Sakilay   1 , MSc ;   Shyamashree Sinha   1 , MSc, MD ;   Gary Brady   4 , DPH ;   Marcia Wright   4 , PharmD ;   Kimberly Nolen   4 , PharmD ;   JoAnn Trainer   4 , PharmD ;   Ross Koppel   1 , PhD ;   Daniel Schlegel   1 , PhD ;   Sashank Kaushik   1 , MD ;   Jane Zhao   1 , MD ;   Buer Song   1 , MD, PhD ;   Edwin Anand   1 , MD

1 Department of Biomedical Informatics, University at Buffalo, Buffalo, NY, United States

2 Bioinformatics Laboratory, Department of Veterans Affairs, VA Western New York Healthcare System, Buffalo, NY, United States

3 School of Engineering, University of Southern Denmark, Odense, Denmark

4 Pfizer, Inc., New York, NY, United States

Corresponding Author:

  • Peter L Elkin, MD
  • Department of Biomedical Informatics
  • University at Buffalo
  • 77 Goodell St
  • Suite 5t40
  • Buffalo, NY, 14203
  • United States
  • Phone: 1 5073581341
  • Email: elkinp@buffalo.edu