About: Effect of machine learning re-sampling techniques for imbalanced datasets in 18F-FDG PET-based radiomics model on prognostication performance in cohorts of head and neck cancer patients

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About: Effect of machine learning re-sampling techniques for imbalanced datasets in 18F-FDG PET-based radiomics model on prognostication performance in cohorts of head and neck cancer patients Goto Sponge NotDistinct Permalink

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scientific article published on 06 April 2020

Attributes	Values
rdf:type	Item
description	wetenschappelijk artikel (nl) article scientifique publié en 2020 (fr) artículu científicu espublizáu n'abril de 2020 (ast) im April 2020 veröffentlichter wissenschaftlicher Artikel (de) наукова стаття, опублікована 6 квітня 2020 (uk) scientific article published on 06 April 2020 (en)
publication date	wds:Q91612843-10E8142B-83B6-46C0-85FD-7DDF1A2B47F4
publication date	2020-04-06 00:00:00Z (xsd:dateTime)
author name string	wds:Q91612843-A9104CEE-627E-47C3-B5B6-51024B64209A
author name string	Chenyi Xie
rdfs:label	Effect of machine learning re-sampling techniques for imbalanced datasets in 18F-FDG PET-based radiomics model on prognostication performance in cohorts of head and neck cancer patients (en) Effect of machine learning re-sampling techniques for imbalanced datasets in 18F-FDG PET-based radiomics model on prognostication performance in cohorts of head and neck cancer patients (nl)
skos:prefLabel	Effect of machine learning re-sampling techniques for imbalanced datasets in 18F-FDG PET-based radiomics model on prognostication performance in cohorts of head and neck cancer patients (en) Effect of machine learning re-sampling techniques for imbalanced datasets in 18F-FDG PET-based radiomics model on prognostication performance in cohorts of head and neck cancer patients (nl)
name	Effect of machine learning re-sampling techniques for imbalanced datasets in 18F-FDG PET-based radiomics model on prognostication performance in cohorts of head and neck cancer patients (en) Effect of machine learning re-sampling techniques for imbalanced datasets in 18F-FDG PET-based radiomics model on prognostication performance in cohorts of head and neck cancer patients (nl)
author	wds:Q91612843-5228FC07-8552-454B-95F4-647F14EF4AAB wds:Q91612843-6F28C3B6-4D44-4900-B33E-147B3E063E05 wds:Q91612843-7069A2C8-2557-4A34-BDD0-27DF8C5D1624 wds:Q91612843-7FE98ACA-00C7-493F-828B-D6F26A2A87BA wds:Q91612843-AE0A0C97-C6D6-40B7-AE2B-53E4BE0466BF wds:Q91612843-F45DC85E-E1B6-4508-8C4E-2D02F87D8F46
author	Joshua Ho Herbert Pang Elaine Yuen Phin Lee Keith Chiu Richard Du Varut Vardhanabhuti
title	wds:Q91612843-C0EFE152-21C7-4112-A3F4-302D067A9AA7
title	Effect of machine learning re-sampling techniques for imbalanced datasets in 18F-FDG PET-based radiomics model on prognostication performance in cohorts of head and neck cancer patients (en)
instance of	wds:Q91612843-D292CC61-890B-4460-B115-1B2DFC84389E
instance of	scholarly article
main subject	wds:Q91612843-46A20128-8926-487A-BBFD-D8CDD37FD292 wds:Q91612843-4E1FC36B-E4D7-4483-9EE6-9D8667BDD3AD wds:Q91612843-DA593251-7F1A-4B88-9960-CEE125C715EE wds:Q91612843-FD5F6600-AF63-4936-B34E-BCD714F27277
main subject	machine learning head and neck cancer head and neck cancer head and neck carcinoma
PubMed ID	wds:Q91612843-BDFA0DBF-1DFD-4BA0-8E26-AE5377F1AC12
PubMed ID	http://rdf.ncbi.nlm.nih.gov/pubchem/reference/32253486
PubMed ID	32253486
published in	wds:Q91612843-C3A37CAD-14D5-43E5-83E1-AF809FF3DDBF
published in	European Journal of Nuclear Medicine and Molecular Imaging
DOI	wds:Q91612843-6B8C632F-8E8F-4289-88AD-C0F2F0FE8836
DOI	http://dx.doi.org/10.1007/S00259-020-04756-4
DOI	10.1007/S00259-020-04756-4
is about of	https://www.wikidata.org/wiki/Special:EntityData/Q91612843
is cites work of	Discrimination of pulmonary ground-glass opacity changes in COVID-19 and non-COVID-19 patients using CT radiomics analysis
is cites work of	wds:Q99595495-6241A445-3FFC-418D-9B1F-58CADAAD8CC8

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