SEANN: A Domain-Informed Neural Network for Epidemiological Insights

TL;DR

SEANN introduces a domain-informed neural network that integrates pooled effect sizes from meta-analyses to improve epidemiological predictions and interpretability, especially in data-scarce scenarios.

Contribution

The paper presents SEANN, a novel neural network that incorporates domain-specific knowledge via pooled effect sizes to enhance epidemiological modeling.

Findings

Significant improvement in predictive generalizability.

Enhanced scientific plausibility of learned relationships.

Effective in scarce and noisy data environments.

Abstract

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 direct integration within the learning procedure using a custom loss, we experimentally demonstrate significant improvements in the generalizability of predictive performances and the scientific plausibility of extracted relationships compared to a domain-knowledge agnostic neural network in a scarce and noisy data setting.