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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"

Wei-Hsin Chen, Sheau-Ling Hsieh, Kai-Ping Hsu, Han-Ping Chen, Xing-Yu Su, Yi-Ju Tseng, Yin-Hsiu Chien, Wuh-Liang Hwu, Feipei Lai

J Med Internet Res 2013 (May 23); 15(5):e98 

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. 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
    CrossRef
  2. . Screening newborns for metabolic disorders based on targeted metabolomics using tandem mass spectrometry. Annals of Pediatric Endocrinology & Metabolism 2015;20(3):119
    CrossRef
  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
    CrossRef
  4. 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
    CrossRef
  5. 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
    CrossRef
  6. Parveen A, Mustafa SH, Yadav P, Kumar A. Applications of Machine Learning in miRNA Discovery and Target Prediction. Current Genomics 2020;20(8):537
    CrossRef
  7. 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
    CrossRef
  8. Zhu Z, Gu J, Genchev GZ, Cai X, Wang Y, Guo J, Tian G, Lu H. Improving the Diagnosis of Phenylketonuria by Using a Machine Learning–Based Screening Model of Neonatal MRM Data. Frontiers in Molecular Biosciences 2020;7
    CrossRef
  9. Shchelochkov OA, Manoli I, Juneau P, Sloan JL, Ferry S, Myles J, Schoenfeld M, Pass A, McCoy S, Van Ryzin C, Wenger O, Levin M, Zein W, Huryn L, Snow J, Chlebowski C, Thurm A, Kopp JB, Chen KY, Venditti CP. Severity modeling of propionic acidemia using clinical and laboratory biomarkers. Genetics in Medicine 2021;23(8):1534
    CrossRef
  10. Chen N, Wang H, Wu B, Jiang J, Yang J, Tang L, He H, Linghu D. Using random forest to detect multiple inherited metabolic diseases simultaneously based on GC-MS urinary metabolomics. Talanta 2021;235:122720
    CrossRef
  11. Mak J, Peng G, Le A, Gandotra N, Enns GM, Scharfe C, Cowan TM. Validation of a targeted metabolomics panel for improved second‐tier newborn screening. Journal of Inherited Metabolic Disease 2023;46(2):194
    CrossRef
  12. Song Y, Yin Z, Zhang C, Hao S, Li H, Wang S, Yang X, Li Q, Zhuang D, Zhang X, Cao Z, Ma X. Random forest classifier improving phenylketonuria screening performance in two Chinese populations. Frontiers in Molecular Biosciences 2022;9
    CrossRef
  13. Zaunseder E, Haupt S, Mütze U, Garbade SF, Kölker S, Heuveline V. Opportunities and challenges in machine learning‐based newborn screening—A systematic literature review. JIMD Reports 2022;63(3):250
    CrossRef
  14. Chen X, Cheng G, Wang FL, Tao X, Xie H, Xu L. Machine and cognitive intelligence for human health: systematic review. Brain Informatics 2022;9(1)
    CrossRef
  15. Usha Rani G, Kadali S, Kurma Reddy B, Shaheena D, Naushad SM. Application of machine learning tools and integrated OMICS for screening and diagnosis of inborn errors of metabolism. Metabolomics 2023;19(5)
    CrossRef
  16. Keles E, Bagci U. The past, current, and future of neonatal intensive care units with artificial intelligence: a systematic review. npj Digital Medicine 2023;6(1)
    CrossRef
  17. Panchbudhe SA, Shivkar RR, Banerjee A, Deshmukh P, Maji BK, Kadam CY. Improving newborn screening in India: Disease gaps and quality control. Clinica Chimica Acta 2024;557:117881
    CrossRef

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

  1. Miletić Vukajlović J, Panić-Janković T. Mass Spectrometry in Life Sciences and Clinical Laboratory. 2021. Chapter 5
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