An informed machine learning based environmental risk score for hypertension in European adults.

Journal: Artificial intelligence in medicine
PMID: 40311152

Abstract

BACKGROUND: The exposome framework seeks to unravel the cumulated effects of environmental exposures on health. However, existing methods struggle with challenges including multicollinearity, non-linearity and confounding. To address these limitations, we introduce SEANN (Summary Effect Adjusted Neural Network) a novel approach that integrates pooled effect sizes-a form of domain knowledge-with neural networks to improve the analysis and interpretation of hypertension risk factors.

Authors

  • Jean-Baptiste Guimbaud
    ISGlobal, Barcelona, Spain; University of Lyon, UCBL, CNRS, INSA Lyon, LIRIS, UMR5205, F-69622 Villeurbanne, France; Meersens, Lyon, France. Electronic address: jeanbaptiste.guimbaud@gmail.com.
  • Emilie Calabre
    Meersens, Lyon, France. Electronic address: emilie@meersens.com.
  • Rafael de Cid
    Genomes for Life-GCAT lab. CORE program. Germans Trias I Pujol Research Institute (IGTP), Camí de les Escoles, s/n, Badalona 08916, Catalonia, Spain.
  • Camille Lassale
    Hospital del Mar Medical Research Institute (IMIM), Spain.
  • Manolis Kogevinas
    Barcelona Institute for Global Health (ISGlobal), C/ del Dr. Aiguader, 88, Barcelona 08003, Catalonia, Spain.
  • Léa Maître
    ISGlobal, Barcelona, Spain. Electronic address: lea.maitre@isglobal.org.
  • Rémy Cazabet
    University of Lyon, UCBL, CNRS, INSA Lyon, LIRIS, UMR5205, F-69622 Villeurbanne, France. Electronic address: remy.cazabet@liris.cnrs.fr.

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