SEANN: A Domain-Informed Neural Network for Epidemiological Insights

2025-01-17

AI Summary

Deep neural networks (DNNs) suffer from poor generalizability and limited interpretability when applied to epidemiological modeling under small-sample, high-noise data regimes. Method: We propose a domain-guided neural network that integrates pooled effect sizes (PES) derived from meta-analysis directly into the DNN’s differentiable loss function—enabling end-to-end, verifiable, knowledge-informed training. Our approach synergistically combines deep learning, eXplainable AI (XAI), and PES-driven custom loss design. Results: Experiments demonstrate substantial improvements in predictive generalization under data scarcity; learned feature relationships align more closely with epidemiological prior knowledge, thereby enhancing scientific credibility and mechanistic interpretability. This work establishes a novel paradigm for knowledge-guided, interpretable AI in public health modeling.

Technology Category

• Humans and AI: Explainable AI (XAI) for Human Understanding
• Machine Learning: Deep Neural Architectures and Foundation Models
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

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.

Problem

• Epidemiology
• Deep Neural Networks
• Data Scarcity

Innovation

• SEANN
• Pooled Effect Size
• Epidemiological Prediction