Your browser doesn't support javascript.
loading
Machine learning improves early prediction of small-for-gestational-age births and reveals nuchal fold thickness as unexpected predictor.
Saw, Shier Nee; Biswas, Arijit; Mattar, Citra Nurfarah Zaini; Lee, Hwee Kuan; Yap, Choon Hwai.
Affiliation
  • Saw SN; Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), Singapore.
  • Biswas A; Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, University of Malaya, Malaysia.
  • Mattar CNZ; Department of Obstetrics and Gynecology, Yong Loo Lin School of Medicine, National University of Singapore, National University Health Systems, Singapore.
  • Lee HK; Department of Obstetrics and Gynecology, Yong Loo Lin School of Medicine, National University of Singapore, National University Health Systems, Singapore.
  • Yap CH; Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), Singapore.
Prenat Diagn ; 41(4): 505-516, 2021 03.
Article in En | MEDLINE | ID: mdl-33462877
Subject(s)
Fulltext
Add to My VHL
Print
XML
PubMed Links
Search on Google

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Infant, Small for Gestational Age / Predictive Value of Tests / Nuchal Translucency Measurement / Machine Learning Type of study: Guideline / Observational_studies / Prognostic_studies / Risk_factors_studies Limits: Female / Humans / Male / Newborn Country/Region as subject: Asia Language: En Journal: Prenat Diagn Year: 2021 Document type: Article Affiliation country: Singapore Country of publication: United kingdom
Fulltext
Add to My VHL
Print
XML
PubMed Links
Search on Google

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Infant, Small for Gestational Age / Predictive Value of Tests / Nuchal Translucency Measurement / Machine Learning Type of study: Guideline / Observational_studies / Prognostic_studies / Risk_factors_studies Limits: Female / Humans / Male / Newborn Country/Region as subject: Asia Language: En Journal: Prenat Diagn Year: 2021 Document type: Article Affiliation country: Singapore Country of publication: United kingdom