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Citing this Article

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Published on 15.01.18 in Vol 6, No 1 (2018): Jan-Mar

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

Works citing "Automating Quality Measures for Heart Failure Using Natural Language Processing: A Descriptive Study in the Department of Veterans Affairs"

Jennifer Hornung Garvin, Youngjun Kim, Glenn Temple Gobbel, Michael E Matheny, Andrew Redd, Bruce E Bray, Paul Heidenreich, Dan Bolton, Julia Heavirland, Natalie Kelly, Ruth Reeves, Megha Kalsy, Mary Kane Goldstein, Stephane M Meystre

JMIR Med Inform 2018 (Jan 15); 6(1):e5 

According to Crossref, the following articles are citing this article (DOI 10.2196/medinform.9150):

(note that this is only a small subset of citations)

  1. Patel SK, George B, Rai V. Artificial Intelligence to Decode Cancer Mechanism: Beyond Patient Stratification for Precision Oncology. Frontiers in Pharmacology 2020;11
    CrossRef
  2. Bergström A, Ehrenberg A, Eldh AC, Graham ID, Gustafsson K, Harvey G, Hunter S, Kitson A, Rycroft-Malone J, Wallin L. The use of the PARIHS framework in implementation research and practice—a citation analysis of the literature. Implementation Science 2020;15(1)
    CrossRef
  3. Xu J, Yang P, Xue S, Sharma B, Sanchez-Martin M, Wang F, Beaty KA, Dehan E, Parikh B. Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives. Human Genetics 2019;138(2):109
    CrossRef
  4. Uijl A, Lund LH, Vaartjes I, Brugts JJ, Linssen GC, Asselbergs FW, Hoes AW, Dahlström U, Koudstaal S, Savarese G. A registry‐based algorithm to predict ejection fraction in patients with heart failure. ESC Heart Failure 2020;7(5):2388
    CrossRef
  5. Khan A, Gurvitz M. Epidemiology of ACHD: What Has Changed and What is Changing?. Progress in Cardiovascular Diseases 2018;61(3-4):275
    CrossRef
  6. Sheikhalishahi S, Miotto R, Dudley JT, Lavelli A, Rinaldi F, Osmani V. Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review. JMIR Medical Informatics 2019;7(2):e12239
    CrossRef
  7. Polanczyk CA, Ruschel KB, Castilho FM, Ribeiro AL. Quality Measures in Heart Failure: the Past, the Present, and the Future. Current Heart Failure Reports 2019;16(1):1
    CrossRef
  8. Kadosh BS, Katz SD, Blecker S. Identification of Patients with Heart Failure in Large Datasets. Heart Failure Clinics 2020;16(4):379
    CrossRef
  9. Khazanie P, Allen LA. Systematizing Heart Failure Population Health. Heart Failure Clinics 2020;16(4):457
    CrossRef
  10. Wagle AA, Isakadze N, Nasir K, Martin SS. Strengthening the Learning Health System in Cardiovascular Disease Prevention: Time to Leverage Big Data and Digital Solutions. Current Atherosclerosis Reports 2021;23(5)
    CrossRef
  11. Madrigal C, Kim J, Jiang L, Lafo J, Bozzay M, Primack J, Correia S, Erqou S, Wu W, Rudolph JL. Delirium and Functional Recovery in Patients Discharged to Skilled Nursing Facilities After Hospitalization for Heart Failure. JAMA Network Open 2021;4(3):e2037968
    CrossRef
  12. Rahman M, Nowakowski S, Agrawal R, Naik A, Sharafkhaneh A, Razjouyan J. Validation of a Natural Language Processing Algorithm for the Extraction of the Sleep Parameters from the Polysomnography Reports. Healthcare 2022;10(10):1837
    CrossRef
  13. . Artificial intelligence and machine learning in precision and genomic medicine. Medical Oncology 2022;39(8)
    CrossRef
  14. Van den Eynde J, Lachmann M, Laugwitz K, Manlhiot C, Kutty S. Successfully implemented artificial intelligence and machine learning applications in cardiology: State-of-the-art review. Trends in Cardiovascular Medicine 2023;33(5):265
    CrossRef
  15. Luther SL, Finch DK, Bouayad L, McCart J, Han L, Dobscha SK, Skanderson M, Fodeh SJ, Hahm B, Lee A, Goulet JL, Brandt CA, Kerns RD. Measuring pain care quality in the Veterans Health Administration primary care setting. Pain 2022;163(6):e715
    CrossRef
  16. Reading Turchioe M, Volodarskiy A, Pathak J, Wright DN, Tcheng JE, Slotwiner D. Systematic review of current natural language processing methods and applications in cardiology. Heart 2022;108(12):909
    CrossRef
  17. Houssein EH, Mohamed RE, Ali AA. Machine Learning Techniques for Biomedical Natural Language Processing: A Comprehensive Review. IEEE Access 2021;9:140628
    CrossRef
  18. Manlhiot C, van den Eynde J, Kutty S, Ross HJ. A Primer on the Present State and Future Prospects for Machine Learning and Artificial Intelligence Applications in Cardiology. Canadian Journal of Cardiology 2022;38(2):169
    CrossRef
  19. Anetta K, Horak A, Wojakowski W, Wita K, Jadczyk T. Deep Learning Analysis of Polish Electronic Health Records for Diagnosis Prediction in Patients with Cardiovascular Diseases. Journal of Personalized Medicine 2022;12(6):869
    CrossRef
  20. Zanotto BS, Beck da Silva Etges AP, dal Bosco A, Cortes EG, Ruschel R, De Souza AC, Andrade CMV, Viegas F, Canuto S, Luiz W, Ouriques Martins S, Vieira R, Polanczyk C, André Gonçalves M. Stroke Outcome Measurements From Electronic Medical Records: Cross-sectional Study on the Effectiveness of Neural and Nonneural Classifiers. JMIR Medical Informatics 2021;9(11):e29120
    CrossRef
  21. Ledziński , Grześk G. Artificial Intelligence Technologies in Cardiology. Journal of Cardiovascular Development and Disease 2023;10(5):202
    CrossRef
  22. van Assen M, Tariq A, Razavi AC, Yang C, Banerjee I, De Cecco CN. Fusion Modeling: Combining Clinical and Imaging Data to Advance Cardiac Care. Circulation: Cardiovascular Imaging 2023;16(12)
    CrossRef

According to Crossref, the following books are citing this article (DOI 10.2196/medinform.9150):

  1. Rüping S, Sander J. Gesundheit digital. 2019. Chapter 2:15
    CrossRef
  2. Tariq A, Santos T, Banerjee I. Artificial Intelligence in Cardiothoracic Imaging. 2022. Chapter 23:231
    CrossRef
  3. Afşin Y, Taşkaya Temizel T. Persuasive Technology. 2024. Chapter 1:1
    CrossRef