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Citing this Article

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Published on 23.05.13 in Vol 15, No 5 (2013): May

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

Works citing "Web-Based Newborn Screening System for Metabolic Diseases: Machine Learning Versus Clinicians"

According to Crossref, the following articles are citing this article (DOI 10.2196/jmir.2495):

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

  1. Peng G, Tang Y, Cowan TM, Enns GM, Zhao H, Scharfe C. Reducing False-Positive Results in Newborn Screening Using Machine Learning. International Journal of Neonatal Screening 2020;6(1):16
  2. Parveen A, Mustafa SH, Yadav P, Kumar A. Applications of Machine Learning in miRNA Discovery and Target Prediction. Current Genomics 2020;20(8):537
  3. Yang Q, Xu L, Tang L, Yang J, Wu B, Chen N, Jiang J, Yu R. Simultaneous detection of multiple inherited metabolic diseases using GC-MS urinary metabolomics by chemometrics multi-class classification strategies. Talanta 2018;186:489
  4. Segundo U, Aldámiz-Echevarría L, López-Cuadrado J, Buenestado D, Andrade F, Pérez TA, Barrena R, Pérez-Yarza EG, Pikatza JM. Improvement of newborn screening using a fuzzy inference system. Expert Systems with Applications 2017;78:301
  5. Ho T, Huang C, Lin C, Lai F, Ding J, Ho Y, Hung C. A Telesurveillance System With Automatic Electrocardiogram Interpretation Based on Support Vector Machine and Rule-Based Processing. JMIR Medical Informatics 2015;3(2):e21
  6. Yoon H. Screening newborns for metabolic disorders based on targeted metabolomics using tandem mass spectrometry. Annals of Pediatric Endocrinology & Metabolism 2015;20(3):119
  7. Chen W, Wu Z, Yang C, Liao Z, Lai F, Hsu C, Sun W. Pulse Analysis System with a Novice Periodic Function Examination Method on Sepsis Survival Prediction. Procedia Computer Science 2014;37:317