Time to reality check the promises of machine learning-powered precision medicine

Autor: Jack Wilkinson, PhD, Kellyn F Arnold, PhD, Eleanor J Murray, ScD, Maarten van Smeden, PhD, Kareem Carr, MSc, Rachel Sippy, PhD, Marc de Kamps, PhD, Andrew Beam, PhD, Stefan Konigorski, PhD, Christoph Lippert, ProfPhD, Mark S Gilthorpe, ProfPhD, Peter W G Tennant, PhD

Publikováno v: The Lancet: Digital Health, Vol 2, Iss 12, Pp e677-e680 (2020)

Summary: Machine learning methods, combined with large electronic health databases, could enable a personalised approach to medicine through improved diagnosis and prediction of individual responses to therapies. If successful, this strategy would re

Externí odkaz: https://doaj.org/article/ba0bc357105946ffbe5e017ed738eb9e

Reply to WC Willett et al

Autor: Georgia D Tomova, Kellyn F Arnold, Mark S Gilthorpe, Peter WG Tennant

Publikováno v: The American Journal of Clinical Nutrition. 116:609-610

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::afe10312795d94657222f73cbc8c2a71
https://doi.org/10.1093/ajcn/nqac115

OP02 A counterfactual analysis of the effects of lockdown timing on cases of COVID-19 across Europe

Autor: Mark S. Gilthorpe, Kellyn F Arnold, Alison J. Heppenstall, Peter W.G. Tennant

Publikováno v: SSM Annual Scientific Meeting.

BackgroundThroughout the COVID-19 pandemic, many countries have relied on population-wide ‘lockdown’ measures to cut chains of transmission and prevent health services from being overwhelmed. Because of their substantial social and economic costs

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::0c1911862c4634320020837adfe07ef1
https://doi.org/10.1136/jech-2021-ssmabstracts.2

Analyses of ‘change scores’ do not estimate causal effects in observational data

Autor: Peter W. G. Tennant, George T. H. Ellison, Mark S. Gilthorpe, Kellyn F Arnold

Background: In longitudinal data, it is common to create 'change scores' by subtracting measurements taken at baseline from those taken at follow-up, and then to analyse the resulting 'change' as the outcome variable. In observational data, this appr

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1a3bd2e6806459acf70d2e722ecf16f8

Use of directed acyclic graphs (DAGs) to identify confounders in applied health research: review and recommendations

Autor: Laurie Berrie, George T. H. Ellison, Eleanor J Murray, Kellyn F Arnold, Georgia D Tomova, Peter W. G. Tennant, Wendy J Harrison, Sarah C. Gadd, Johannes Textor, Matthew P. Fox, Mark S. Gilthorpe, Claire Keeble, Lynsie R Ranker

Publikováno v: International Journal of Epidemiology, 50, 2, pp. 620-632
International Journal of Epidemiology
International Journal of Epidemiology, 50, 620-632

Background Directed acyclic graphs (DAGs) are an increasingly popular approach for identifying confounding variables that require conditioning when estimating causal effects. This review examined the use of DAGs in applied health research to inform r

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2bc8c0c61c39545a47ace07156403ff1
https://repository.ubn.ru.nl/handle/2066/244722