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Citing this Article

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Published on 25.04.18 in Vol 4, No 2 (2018): Apr-Jun

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

Works citing "Clinical Relation Extraction Toward Drug Safety Surveillance Using Electronic Health Record Narratives: Classical Learning Versus Deep Learning"

According to Crossref, the following articles are citing this article (DOI 10.2196/publichealth.9361):

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

  1. Alimova I, Tutubalina E. Multiple features for clinical relation extraction: A machine learning approach. Journal of Biomedical Informatics 2020;103:103382
    CrossRef
  2. Siefridt C, Grosjean J, Lefebvre T, Rollin L, Darmoni S, Schuers M. Evaluation of automatic annotation by a multi-terminological concepts extractor within a corpus of data from family medicine consultations. International Journal of Medical Informatics 2020;133:104009
    CrossRef
  3. Christopoulou F, Tran TT, Sahu SK, Miwa M, Ananiadou S. Adverse drug events and medication relation extraction in electronic health records with ensemble deep learning methods. Journal of the American Medical Informatics Association 2020;27(1):39
    CrossRef
  4. Chen T, Wu M, Li H. A general approach for improving deep learning-based medical relation extraction using a pre-trained model and fine-tuning. Database 2019;2019
    CrossRef
  5. Li F, Yu H. An investigation of single-domain and multidomain medication and adverse drug event relation extraction from electronic health record notes using advanced deep learning models. Journal of the American Medical Informatics Association 2019;26(7):646
    CrossRef
  6. Tang Y, Yang J, Ang PS, Dorajoo SR, Foo B, Soh S, Tan SH, Tham MY, Ye Q, Shek L, Sung C, Tung A. Detecting adverse drug reactions in discharge summaries of electronic medical records using Readpeer. International Journal of Medical Informatics 2019;128:62
    CrossRef
  7. Dandala B, Joopudi V, Devarakonda M. Adverse Drug Events Detection in Clinical Notes by Jointly Modeling Entities and Relations Using Neural Networks. Drug Safety 2019;42(1):135
    CrossRef
  8. 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
    CrossRef
  9. Jin Y, Li F, Vimalananda VG, Yu H. Automatic Detection of Hypoglycemic Events From the Electronic Health Record Notes of Diabetes Patients: Empirical Study. JMIR Medical Informatics 2019;7(4):e14340
    CrossRef
  10. Young JC, Conover MM, Jonsson Funk M. Measurement Error and Misclassification in Electronic Medical Records: Methods to Mitigate Bias. Current Epidemiology Reports 2018;5(4):343
    CrossRef
  11. Li F, Liu W, Yu H. Extraction of Information Related to Adverse Drug Events from Electronic Health Record Notes: Design of an End-to-End Model Based on Deep Learning. JMIR Medical Informatics 2018;6(4):e12159
    CrossRef

According to Crossref, the following books are citing this article (DOI 10.2196/publichealth.9361)

:
  1. Mendonca E, Tachinardi U. Personalized and Precision Medicine Informatics. 2020. Chapter 14:199
    CrossRef
  2. Liu F, Weng C, Yu H. Clinical Research Informatics. 2019. Chapter 17:357
    CrossRef