About: Letter to the editor: a response to Ming's study on machine learning techniques for personalized breast cancer risk prediction

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scientific article published on 10 February 2020

Attributes	Values
rdf:type	Item
description	wetenschappelijk artikel (nl) article scientifique publié en 2020 (fr) artículu científicu espublizáu en febreru de 2020 (ast) scientific article published on 10 February 2020 (en) 2020 թվականի փետրվարի 10-ին հրատարակված գիտական հոդված (hy) наукова стаття, опублікована 10 лютого 2020 (uk)
publication date	wds:Q89635615-21D66B38-4FA9-4462-A8CA-EBFFF172EDEC
publication date	2020-02-10 00:00:00Z (xsd:dateTime)
cites work	wds:Q89635615-630FCE11-D19F-4B90-B833-3438831B2E3D wds:Q89635615-7E03C440-FD0F-41F0-A84A-692D3F03C166 wds:Q89635615-D3073FB1-5EB2-401D-A8A0-13D4EA82BE60 wds:Q89635615-E2E63039-3067-49E7-8A36-16868416A0DF wds:Q89635615-F87B8652-B4DD-4D73-B61B-497ED181B18B
cites work	Prediction models need appropriate internal, internal-external, and external validation External validation of new risk prediction models is infrequent and reveals worse prognostic discrimination A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models A simple, step-by-step guide to interpreting decision curve analysis Machine learning techniques for personalized breast cancer risk prediction: comparison with the BCRAT and BOADICEA models.
author name string	wds:Q89635615-4AF02E31-130C-43B6-B063-A7B6ADF14924 wds:Q89635615-DEE88647-AA16-43DE-BE7A-7E6C3AA88673
author name string	Ewout W Steyerberg Luigi Mariani
rdfs:label	Letter to the editor: a response to Ming's study on machine learning techniques for personalized breast cancer risk prediction (en) Letter to the editor: a response to Ming's study on machine learning techniques for personalized breast cancer risk prediction (nl)
skos:prefLabel	Letter to the editor: a response to Ming's study on machine learning techniques for personalized breast cancer risk prediction (en) Letter to the editor: a response to Ming's study on machine learning techniques for personalized breast cancer risk prediction (nl)
name	Letter to the editor: a response to Ming's study on machine learning techniques for personalized breast cancer risk prediction (en) Letter to the editor: a response to Ming's study on machine learning techniques for personalized breast cancer risk prediction (nl)
author	wds:Q89635615-57A2CF4C-7ADA-4BA7-86C5-603E28B6C9C8 wds:Q89635615-A77CD004-3BDB-4727-AEAC-9886767E279D wds:Q89635615-B1801659-29C0-474B-9DF0-5C1C91FD36CE
author	Douglas F. Easton Antonis C Antoniou Daniele Giardiello
title	wds:Q89635615-F9E99AED-B3C0-43E3-B9CB-3DDE1E51195E
title	Letter to the editor: a response to Ming's study on machine learning techniques for personalized breast cancer risk prediction (en)
page(s)	wds:Q89635615-07D0FA48-F4C1-4D1C-9B04-1F40F993EDEE
page(s)	17
instance of	wds:Q89635615-AC742A97-5CBF-42EA-AB75-29745F376E76
instance of	scholarly article
main subject	wds:Q89635615-FA97B1F9-FF42-4FB9-A5B5-D25F15362286
main subject	machine learning
PubMed ID	wds:Q89635615-1DE6E92C-FF6F-4542-AD2A-8F91C689778B
PubMed ID	http://rdf.ncbi.nlm.nih.gov/pubchem/reference/32041655
PubMed ID	32041655
published in	wds:Q89635615-2BFDAEDF-9B55-4C35-B026-6F007363F568
published in	Breast Cancer Research
issue	wds:Q89635615-D52DD5B1-DCC2-472E-94C3-11E6ECF5F8A2
volume	wds:Q89635615-68566ADF-5BF6-4953-9041-683C1CAC701B
issue	1
volume	22
DOI	wds:Q89635615-7BF8F8FE-4F5C-43CA-B032-E886AFD18D34
DOI	http://dx.doi.org/10.1186/S13058-020-1255-4
DOI	10.1186/S13058-020-1255-4
PMCID	wds:Q89635615-F9594903-3FC9-427D-9768-B46C122AEAEA
PMCID	7011440
is about of	https://www.wikidata.org/wiki/Special:EntityData/Q89635615
is cites work of	Letter to the editor: Response to Giardiello D, Antoniou AC, Mariani L, Easton DF, Steyerberg EW
is cites work of	wds:Q91813371-89ECF5A3-0C5E-47AF-BE01-5A60E38E2DC3

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