Zobrazeno 1 - 10
of 556
pro vyhledávání: '"Rijnbeek, P."'
Autor:
John, Luis H., Kim, Chungsoo, Kors, Jan A., Chang, Junhyuk, Morgan-Cooper, Hannah, Desai, Priya, Pang, Chao, Rijnbeek, Peter R., Reps, Jenna M., Fridgeirsson, Egill A.
Background: Conventional prediction methods such as logistic regression and gradient boosting have been widely utilized for disease onset prediction for their reliability and interpretability. Deep learning methods promise enhanced prediction perform
Externí odkaz:
http://arxiv.org/abs/2410.10505
Autor:
Luis H. John, Egill A. Fridgeirsson, Jan A. Kors, Jenna M. Reps, Ross D. Williams, Patrick B. Ryan, Peter R. Rijnbeek
Publikováno v:
BMC Medicine, Vol 22, Iss 1, Pp 1-12 (2024)
Abstract Background A prediction model can be a useful tool to quantify the risk of a patient developing dementia in the next years and take risk-factor-targeted intervention. Numerous dementia prediction models have been developed, but few have been
Externí odkaz:
https://doaj.org/article/fc208c44b82f4bab9139744e5d70852e
Autor:
Rekkas, Alexandros, Rijnbeek, Peter R., Kent, David M., Steyerberg, Ewout W., van Klaveren, David
Objective: To compare different risk-based methods for optimal prediction of treatment effects. Methods: We simulated RCT data using diverse assumptions for the average treatment effect, a baseline prognostic index of risk (PI), the shape of its inte
Externí odkaz:
http://arxiv.org/abs/2205.01717
Autor:
Lo Re III V, Cocoros NM, Hubbard RA, Dutcher SK, Newcomb CW, Connolly JG, Perez-Vilar S, Carbonari DM, Kempner ME, Hernández-Muñoz JJ, Petrone AB, Pishko AM, Rogers Driscoll ME, Brash JT, Burnett S, Cohet C, Dahl M, DeFor TA, Delmestri A, Djibo DA, Duarte-Salles T, Harrington LB, Kampman M, Kuntz JL, Kurz X, Mercadé-Besora N, Pawloski PA, Rijnbeek PR, Seager S, Steiner CA, Verhamme K, Wu F, Zhou Y, Burn E, Paterson JM, Prieto-Alhambra D
Publikováno v:
Clinical Epidemiology, Vol Volume 16, Pp 71-89 (2024)
Vincent Lo Re III,1,2,* Noelle M Cocoros,3,4,* Rebecca A Hubbard,2 Sarah K Dutcher,5 Craig W Newcomb,2 John G Connolly,3,4 Silvia Perez-Vilar,5 Dena M Carbonari,2 Maria E Kempner,3,4 José J Hernández-Muñoz,5 Andrew B Petrone,3,4 Allyson M
Externí odkaz:
https://doaj.org/article/c9ce334516fa4bcebe0b53acfa4df4d6
Autor:
Daniel Prieto-Alhambra, Talita Duarte-Salles, Edward Burn, Rae Woong Park, Jan A Kors, Carlen Reyes, Jerry A Krishnan, Peter R Rijnbeek, Guy G Brusselle, Aniek F Markus, Markus Haug, Chungsoo Kim, Raivo Kolde, Youngsoo Lee, Hae-Sim Park, Katia MC Verhamme
Publikováno v:
BMJ Open Respiratory Research, Vol 11, Iss 1 (2024)
Background There is a lack of knowledge on how patients with asthma or chronic obstructive pulmonary disease (COPD) are globally treated in the real world, especially with regard to the initial pharmacological treatment of newly diagnosed patients an
Externí odkaz:
https://doaj.org/article/5afef2731e734ebcadc1cb028255c8ff
Autor:
Vincent M.I. Voorbrood, Evelien I.T. de Schepper, Arthur M. Bohnen, Marit F.E. Ruiterkamp, Peter R. Rijnbeek, Patrick J.E. Bindels
Publikováno v:
BMC Primary Care, Vol 25, Iss 1, Pp 1-9 (2024)
Abstract Background In the adult population, about 50% have hypertension, a risk factor for cardiovascular disease and subsequent premature death. Little is known about the quality of the methods used to diagnose hypertension in primary care. Objecti
Externí odkaz:
https://doaj.org/article/2777344b1e4c47d29e2934c1fb8c7efd
Publikováno v:
Journal of Big Data, Vol 11, Iss 1, Pp 1-17 (2024)
Abstract Background There is currently no consensus on the impact of class imbalance methods on the performance of clinical prediction models. We aimed to empirically investigate the impact of random oversampling and random undersampling, two commonl
Externí odkaz:
https://doaj.org/article/16e1370b11874d648d085a5e82d580c9
Publikováno v:
BMC Medical Research Methodology, Vol 23, Iss 1, Pp 1-10 (2023)
Abstract Background Deep learning models have had a lot of success in various fields. However, on structured data they have struggled. Here we apply four state-of-the-art supervised deep learning models using the attention mechanism and compare again
Externí odkaz:
https://doaj.org/article/fa3c3585850f414f872a73aa971dd1cd
Autor:
Raventós B, Fernández-Bertolín S, Aragón M, Voss EA, Blacketer C, Méndez-Boo L, Recalde M, Roel E, Pistillo A, Reyes C, van Sandijk S, Halvorsen L, Rijnbeek PR, Burn E, Duarte-Salles T
Publikováno v:
Clinical Epidemiology, Vol Volume 15, Pp 969-986 (2023)
Berta Raventós,1,2,* Sergio Fernández-Bertolín,1,* María Aragón,1 Erica A Voss,3– 5 Clair Blacketer,3– 5 Leonardo Méndez-Boo,6 Martina Recalde,1 Elena Roel,1,2 Andrea Pistillo,1,7 Carlen Reyes,1 Sebastiaan van Sandijk,8 Lars Halvors
Externí odkaz:
https://doaj.org/article/7f112681f1c44914935f562158cd320b
Autor:
Rekkas, Alexandros, van Klaveren, David, Ryan, Patrick B., Steyerberg, Ewout W., Kent, David M., Rijnbeek, Peter R.
The Predictive Approaches to Treatment Effect Heterogeneity statement focused on baseline risk as a robust predictor of treatment effect and provided guidance on risk-based assessment of treatment effect heterogeneity in the RCT setting. The aim of t
Externí odkaz:
http://arxiv.org/abs/2010.06430