This paper is in the following e-collection/theme issue:

Artificial Intelligence (720) Machine Learning (1058) Secondary Use of Clinical Data for Research and Surveillance (260) Public Health Informatics (42) Natural Language Processing (509) Electronic Health Records (810) Stroke (119) Cardiac Arrhythmia, Atrial Fibrillation (67) Public (e)Health (379)

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

Authors of this article:

Peter L Elkin 1, 2, 3 Author Orcid Image ;   Sarah Mullin 1 Author Orcid Image ;   Jack Mardekian 4 Author Orcid Image ;   Christopher Crowner 1 Author Orcid Image ;   Sylvester Sakilay 1 Author Orcid Image ;   Shyamashree Sinha 1 Author Orcid Image ;   Gary Brady 4 Author Orcid Image ;   Marcia Wright 4 Author Orcid Image ;   Kimberly Nolen 4 Author Orcid Image ;   JoAnn Trainer 4 Author Orcid Image ;   Ross Koppel 1 Author Orcid Image ;   Daniel Schlegel 1 Author Orcid Image ;   Sashank Kaushik 1 Author Orcid Image ;   Jane Zhao 1 Author Orcid Image ;   Buer Song 1 Author Orcid Image ;   Edwin Anand 1 Author Orcid Image
Article Authors Cited by (5) Tweetations (19) Metrics

Journals

  1. Zhao Y, Cao L, Zhao Y, Wang F, Xie L, Xing H, Wang Q. Medical record data-enabled machine learning can enhance prediction of left atrial appendage thrombosis in nonvalvular atrial fibrillation. Thrombosis Research 2023;223:174 View
  2. Li S, Deng L, Zhang X, Chen L, Yang T, Qi Y, Jiang T. Deep Phenotyping of Chinese Electronic Health Records by Recognizing Linguistic Patterns of Phenotypic Narratives With a Sequence Motif Discovery Tool: Algorithm Development and Validation. Journal of Medical Internet Research 2022;24(6):e37213 View
  3. Shipley E, Joddrell M, Lip G, Zheng Y. Bridging the Gap Between Artificial Intelligence Research and Clinical Practice in Cardiovascular Science: What the Clinician Needs to Know. Arrhythmia & Electrophysiology Review 2022;11 View
  4. Chiavi D, Haag C, Chan A, Kamm C, Sieber C, Stanikić M, Rodgers S, Pot C, Kesselring J, Salmen A, Rapold I, Calabrese P, Manjaly Z, Gobbi C, Zecca C, Walther S, Stegmayer K, Hoepner R, Puhan M, von Wyl V. The Real-World Experiences of Persons With Multiple Sclerosis During the First COVID-19 Lockdown: Application of Natural Language Processing. JMIR Medical Informatics 2022;10(11):e37945 View
  5. Jaques-Albuquerque L, dos Anjos-Martins E, Torres-Nunes L, Valério-Penha A, Coelho-Oliveira A, da Silva Sarandy V, Reis-Silva A, Seixas A, Bernardo-Filho M, Taiar R, de Sá-Caputo D. Effectiveness of Using the FreeStyle Libre® System for Monitoring Blood Glucose during the COVID-19 Pandemic in Diabetic Individuals: Systematic Review. Diagnostics 2023;13(8):1499 View