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

Clinical Information and Decision Making (1407) Digital Health, Telehealth and e-Innovation in Clinical Settings (401)

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

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

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

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

Authors of this article:

Wei-Hsin Chen1 ;   Sheau-Ling Hsieh2 ;   Kai-Ping Hsu3 ;   Han-Ping Chen4 ;   Xing-Yu Su1 ;   Yi-Ju Tseng1 ;   Yin-Hsiu Chien5 ;   Wuh-Liang Hwu5 ;   Feipei Lai1, 4, 6
Article Authors Cited by (20) Tweetations (9) Metrics

Journals

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  5. Segundo U, Aldámiz-Echevarría L, López-Cuadrado J, Buenestado D, Andrade F, Pérez T, Barrena R, Pérez-Yarza E, Pikatza J. Improvement of newborn screening using a fuzzy inference system. Expert Systems with Applications 2017;78:301 View
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  9. Shchelochkov O, Manoli I, Juneau P, Sloan J, 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 J, Chen K, Venditti C. Severity modeling of propionic acidemia using clinical and laboratory biomarkers. Genetics in Medicine 2021;23(8):1534 View
  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 View
  11. Mak J, Peng G, Le A, Gandotra N, Enns G, Scharfe C, Cowan T. Validation of a targeted metabolomics panel for improved second‐tier newborn screening. Journal of Inherited Metabolic Disease 2023;46(2):194 View
  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 View
  13. Zaunseder E, Haupt S, Mütze U, Garbade S, Kölker S, Heuveline V. Opportunities and challenges in machine learning‐based newborn screening—A systematic literature review. JIMD Reports 2022;63(3):250 View
  14. Chen X, Cheng G, Wang F, Tao X, Xie H, Xu L. Machine and cognitive intelligence for human health: systematic review. Brain Informatics 2022;9(1) View
  15. Usha Rani G, Kadali S, Kurma Reddy B, Shaheena D, Naushad S. Application of machine learning tools and integrated OMICS for screening and diagnosis of inborn errors of metabolism. Metabolomics 2023;19(5) View
  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) View
  17. Panchbudhe S, Shivkar R, Banerjee A, Deshmukh P, Maji B, Kadam C. Improving newborn screening in India: Disease gaps and quality control. Clinica Chimica Acta 2024;557:117881 View
  18. Velasco-Aznar A, Rendón-Macías M, Silva-Ramírez H, Gerardo-Del Hoyo M, Cruz-Camino H, Cantú-Reyna C, Moscona-Nissan A, Wollenstein-Seligson D. Resultados del programa de tamiz metabólico en un hospital privado de la Ciudad de México. Revista Mexicana de Pediatría 2023;90(6):221 View
  19. Yin Z, Zhang C, Dong R, Zhang X, Song Y, Hao S, Gai Z, Zhou B, Hui L, Wang S, Xue H, Cao Z, Liu Y, Ma X. Improving methylmalonic acidemia (MMA) screening and MMA genotype prediction using random forest classifier in two Chinese populations. European Journal of Medical Research 2024;29(1) View

Books/Policy Documents

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