Paper Info
Title
L1 Penalized Cox Regression To Characterize Cardiovascular Events In Hypertensive Patients
Abstract
Reliable cardiovascular risk stratification of hypertensive patients is essential to provide appropriate clinical management. However, traditional statistical approaches may ignore important information from datasets and overlook possible interactions among covariates when dealing with high-dimensional data. Regularization techniques, such as least absolute shrinkage and selection operator (LASSO), may improve the prediction accuracy and interpretability of regression models. Our aim is to identify the most relevant features to predict cardiovascular (CV) events in hypertensive patients by using the L1-penalized Cox regression. We use clinical records of 1664 patients of the Mostoles University Hospital whose CV status was determined by clinical variables and biomarkers including body index mass, blood pressure, cholesterol, albumin/ creatinine ratio, and kidney function. By monotonically tuning its regularization parameter, the LASSO approach put forth is able to identify the most predictive features, with "cystanin C-based glomerular filtration" being the single most relevant predictor for CV events.
Year
DOI
Venue
2017
10.22489/CinC.2017.253-209
2017 COMPUTING IN CARDIOLOGY (CINC)
Field
DocType
Volume
Creatinine,Covariate,Renal function,Proportional hazards model,Regression analysis,Internal medicine,Cardiology,Lasso (statistics),Biomarker (medicine),Blood pressure,Medicine
Conference
44
ISSN
Citations 
PageRank 
2325-8861
0
0.34
References 
Authors
0
7
Name
Order
Citations
PageRank
Rafael García-Carretero101.69
Óscar Barquero-Pérez23611.04
Inmaculada Mora-Jiménez37716.31
Cristina Soguero-Ruiz46512.73
Rebeca Goya-Esteban5159.47
Antonio G. Marqués625433.71
Javier Ramos770.78