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

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
  6. Harris J. An AI-Enhanced Electronic Health Record Could Boost Primary Care Productivity. JAMA 2023;330(9):801 View
  7. De Rosario H, Pitarch-Corresa S, Pedrosa I, Vidal-Pedrós M, de Otto-López B, García-Mieres H, Álvarez-Rodríguez L. Applications of Natural Language Processing for the Management of Stroke Disorders: Scoping Review. JMIR Medical Informatics 2023;11:e48693 View
  8. Liang J, He Y, Xie J, Fan X, Liu Y, Wen Q, Shen D, Xu J, Gu S, Lei J. Mining electronic health records using artificial intelligence: Bibliometric and content analyses for current research status and product conversion. Journal of Biomedical Informatics 2023;146:104480 View
  9. Skuban-Eiseler T, Orzechowski M, Denkinger M, Kocar T, Leinert C, Steger F. Artificial Intelligence–Based Clinical Decision Support Systems in Geriatrics: An Ethical Analysis. Journal of the American Medical Directors Association 2023;24(9):1271 View
  10. Laursen M, Pedersen J, Hansen R, Savarimuthu T, Lynggaard R, Vinholt P. Doctors Identify Hemorrhage Better during Chart Review when Assisted by Artificial Intelligence. Applied Clinical Informatics 2023;14(04):743 View
  11. Schopow N, Osterhoff G, Baur D. Applications of the Natural Language Processing Tool ChatGPT in Clinical Practice: Comparative Study and Augmented Systematic Review. JMIR Medical Informatics 2023;11:e48933 View