Journal Article

SEANN: A domain-informed neural network for epidemiological insights

Maître, Léa, Cazabet, Rémy, Guimbaud, Jean-Baptiste, Plantevit, Marc

2025

Overview

In epidemiology, traditional statistical methods such as logistic regression, linear regression, and other parametric models are commonly employed to investigate associations between predictors and health outcomes. However, non-parametric machine learning techniques, such as deep neural networks (DNNs), coupled with explainable AI (XAI) tools, offer new opportunities for this task. Despite their potential, these methods face challenges due to the limited availability of high-quality, high-quantity data in this field. To address these challenges, we introduce SEANN, a novel approach for informed DNNs that leverages a prevalent form of domain-specific knowledge: Pooled Effect Sizes (PES). PESs are commonly found in published Meta-Analysis studies, in different forms, and represent a quantitative form of a scientific consensus. By integrating PES into the training loss, we demonstrate—under controlled simulations—significant improvements in predictive generalization and in the epidemiological plausibility of the learned relationships, relative to a domain-knowledge agnostic neural network.

• Publisher: Public Library of Science,PLOS,Public Library of Science (PLoS)
• Subject: Analysis / Artificial Intelligence / Artificial neural networks / Biology and Life Sciences / Clinical epidemiology / Computer and Information Sciences / Computer Science / Deep Learning / Educational aspects / Epidemiology / Estimates / Explainable artificial intelligence / Health aspects / Health risk assessment / Humans / Knowledge / Life Sciences / Machine Learning / Medicine and Health Sciences / Methods / Neural networks / Neural Networks, Computer / Physical Sciences / Regression analysis / Research and Analysis Methods / Santé publique et épidémiologie / Statistical analysis / Statistical methods / Technology application