Výsledky vyhledávání - "Mark R. van den Bunt"

Akademický článek

Prediction of long-term hospitalisation and all-cause mortality in patients with chronic heart failure on Dutch claims data: a machine learning approach

Autor: Onno P. van der Galiën, René C. Hoekstra, Muhammed T. Gürgöze, Olivier C. Manintveld, Mark R. van den Bunt, Cor J. Veenman, Eric Boersma

Publikováno v: BMC Medical Informatics and Decision Making, Vol 21, Iss 1, Pp 1-13 (2021)

Abstract Background Accurately predicting which patients with chronic heart failure (CHF) are particularly vulnerable for adverse outcomes is of crucial importance to support clinical decision making. The goal of the current study was to examine the

Externí odkaz: https://doaj.org/article/1bbfe8e8bf964465bb8bff38db791fe9

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Akademický článek

Theory-driven classification of reading difficulties from fMRI data using Bayesian latent-mixture models

Autor: Noam Siegelman, Mark R. van den Bunt, Jason Chor Ming Lo, Jay G. Rueckl, Kenneth R. Pugh

Publikováno v: NeuroImage, Vol 242, Iss , Pp 118476- (2021)

Decades of research have led to several competing theories regarding the neural contributors to impaired reading. But how can we know which theory (or theories) identifies the types of markers that indeed differentiate between individuals with readin

Externí odkaz: https://doaj.org/article/11da25f821a14ffbbc52e729b70ed7d7

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Prediction of long-term hospitalisation and all-cause mortality in patients with chronic heart failure on Dutch claims data: a machine learning approach

Autor: Olivier C. Manintveld, Mark R. van den Bunt, Cor J. Veenman, Onno van der Galiën, Muhammed T. Gürgöze, Eric Boersma, René C. Hoekstra

Publikováno v: BMC Medical Informatics and Decision Making, Vol 21, Iss 1, Pp 1-13 (2021)
BMC Medical Informatics and Decision Making, 21(1):303. BioMed Central Ltd.
BMC Medical Informatics and Decision Making

Background Accurately predicting which patients with chronic heart failure (CHF) are particularly vulnerable for adverse outcomes is of crucial importance to support clinical decision making. The goal of the current study was to examine the predictiv

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0bca60a3d2db8ffe0d11420ce3daf0ec
https://doaj.org/article/1bbfe8e8bf964465bb8bff38db791fe9

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