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Citing this Article

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Published on 23.02.18 in Vol 6, No 1 (2018): Jan-Mar

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

Works citing "Characterizing and Managing Missing Structured Data in Electronic Health Records: Data Analysis"

According to Crossref, the following articles are citing this article (DOI 10.2196/medinform.8960):

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

  1. Ford E, Rooney P, Hurley P, Oliver S, Bremner S, Cassell J. Can the Use of Bayesian Analysis Methods Correct for Incompleteness in Electronic Health Records Diagnosis Data? Development of a Novel Method Using Simulated and Real-Life Clinical Data. Frontiers in Public Health 2020;8
    CrossRef
  2. Li R, Chen Y, Ritchie MD, Moore JH. Electronic health records and polygenic risk scores for predicting disease risk. Nature Reviews Genetics 2020;
    CrossRef
  3. Callahan A, Shah NH, Chen JH. Research and Reporting Considerations for Observational Studies Using Electronic Health Record Data. Annals of Internal Medicine 2020;172(11_Supplement):S79
    CrossRef
  4. McGurk KA, Dagliati A, Chiasserini D, Lee D, Plant D, Baricevic-Jones I, Kelsall J, Eineman R, Reed R, Geary B, Unwin RD, Nicolaou A, Keavney BD, Barton A, Whetton AD, Geifman N, Wren J. The use of missing values in proteomic data-independent acquisition mass spectrometry to enable disease activity discrimination. Bioinformatics 2020;36(7):2217
    CrossRef
  5. Goodday S, Kormilitzin A, Vaci N, Liu Q, Cipriani A, Smith T, Nevado-Holgado A. Maximizing the use of social and behavioural information from secondary care mental health electronic health records. Journal of Biomedical Informatics 2020;107:103429
    CrossRef
  6. Ross EG, Jung K, Dudley JT, Li L, Leeper NJ, Shah NH. Predicting Future Cardiovascular Events in Patients With Peripheral Artery Disease Using Electronic Health Record Data. Circulation: Cardiovascular Quality and Outcomes 2019;12(3)
    CrossRef
  7. Jetley G, Zhang H. Electronic health records in IS research: Quality issues, essential thresholds and remedial actions. Decision Support Systems 2019;126:113137
    CrossRef
  8. Krittanawong C, Johnson KW, Rosenson RS, Wang Z, Aydar M, Baber U, Min JK, Tang WHW, Halperin JL, Narayan SM. Deep learning for cardiovascular medicine: a practical primer. European Heart Journal 2019;40(25):2058
    CrossRef
  9. Li R, Chen Y, Moore JH. Integration of genetic and clinical information to improve imputation of data missing from electronic health records. Journal of the American Medical Informatics Association 2019;26(10):1056
    CrossRef
  10. Agor J, Özaltın OY, Ivy JS, Capan M, Arnold R, Romero S. The value of missing information in severity of illness score development. Journal of Biomedical Informatics 2019;97:103255
    CrossRef
  11. Chen R, Stewart WF, Sun J, Ng K, Yan X. Recurrent Neural Networks for Early Detection of Heart Failure From Longitudinal Electronic Health Record Data. Circulation: Cardiovascular Quality and Outcomes 2019;12(10)
    CrossRef
  12. Tantoso E, Wong W, Tay WH, Lee J, Sinha S, Eisenhaber B, Eisenhaber F. Hypocrisy Around Medical Patient Data: Issues of Access for Biomedical Research, Data Quality, Usefulness for the Purpose and Omics Data as Game Changer. Asian Bioethics Review 2019;11(2):189
    CrossRef
  13. Pendergrass SA, Crawford DC. Using Electronic Health Records To Generate Phenotypes For Research. Current Protocols in Human Genetics 2018;:e80
    CrossRef
  14. Verma M, Hontecillas R, Tubau-Juni N, Abedi V, Bassaganya-Riera J. Challenges in Personalized Nutrition and Health. Frontiers in Nutrition 2018;5
    CrossRef

According to Crossref, the following books are citing this article (DOI 10.2196/medinform.8960)

:
  1. Allaart CG, Mondrejevski L, Papapetrou P. Artificial Intelligence Applications and Innovations. 2019. Chapter 11:139
    CrossRef