Cardiometabolic risk estimation using exposome data and machine learning-Reference-Cited by-同舟云学术

Cardiometabolic risk estimation using exposome data and machine learning

Published:2023-11 Issue: Volume:179 Page:105209
ISSN:1386-5056
Container-title:International Journal of Medical Informatics
language:en
Short-container-title:International Journal of Medical Informatics

Author:

Atehortúa Angélica^ORCID,Gkontra Polyxeni^ORCID,Camacho Marina,Diaz Oliver,Bulgheroni Maria,Simonetti Valentina^ORCID,Chadeau-Hyam Marc,Felix Janine F.,Sebert Sylvain,Lekadir Karim

Funder

European Union

Horizon 2020

Publisher

Elsevier BV

Subject

Health Informatics

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1. Contribution of laboratory medicine and emerging technologies to cardiovascular risk reduction via exposome analysis: an opinion of the IFCC Division on Emerging Technologies;Clinical Chemistry and Laboratory Medicine (CCLM);2024-09-02

2. Integrating the exposome and one health approach to national health surveillance: an opportunity for Latin American countries in health preventive management;Frontiers in Public Health;2024-08-14

3. Improving cardiovascular risk prediction with machine learning: a focus on perivascular adipose tissue characteristics;BioMedical Engineering OnLine;2024-08-05

4. Exposomics and Cardiovascular Diseases: A Scoping Review of Machine Learning Approaches;2024-07-19

5. Machine Learning Approaches for Predicting Risk of Cardiometabolic Disease among University Students;Big Data and Cognitive Computing;2024-03-13