Published on 30.11.16 in Vol 4, No 4 (2016): Oct-Dec
Works citing "Finding Important Terms for Patients in Their Electronic Health Records: A Learning-to-Rank Approach Using Expert Annotations"
According to Crossref, the following articles are citing this article (DOI 10.2196/medinform.6373):
(note that this is only a small subset of citations)
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Chen J, Jagannatha AN, Fodeh SJ, Yu H. Ranking Medical Terms to Support Expansion of Lay Language Resources for Patient Comprehension of Electronic Health Record Notes: Adapted Distant Supervision Approach. JMIR Medical Informatics 2017;5(4):e42
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Chen J, Lalor J, Liu W, Druhl E, Granillo E, Vimalananda VG, Yu H. Detecting Hypoglycemia Incidents Reported in Patients’ Secure Messages: Using Cost-Sensitive Learning and Oversampling to Reduce Data Imbalance. Journal of Medical Internet Research 2019;21(3):e11990
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Qassimi S, Abdelwahed EH. The role of collaborative tagging and ontologies in emerging semantic of web resources. Computing 2019;101(10):1489
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Chen J, Yu H. Unsupervised ensemble ranking of terms in electronic health record notes based on their importance to patients. Journal of Biomedical Informatics 2017;68:121
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Kersloot MG, van Putten FJP, Abu-Hanna A, Cornet R, Arts DL. Natural language processing algorithms for mapping clinical text fragments onto ontology concepts: a systematic review and recommendations for future studies. Journal of Biomedical Semantics 2020;11(1)
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Tenorio JM, de Moraes FL, Pisa IT. CHV.br: Exploratory study for the development of a consumer health vocabulary (CHV) supported by a network model for Brazilian Portuguese language. Journal of Information Science 2023;
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According to Crossref, the following books are citing this article (DOI 10.2196/medinform.6373):
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. Social Web and Health Research. 2019. Chapter 6:103
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