Exploring the potential and limitations of deep learning and explainable AI for longitudinal life course analysis

作者：Helen Coupland, Neil Scheidwasser, Alexandros Katsiferis, Megan Davies, Seth Flaxman, Naja Hulvej Rod, Swapnil Mishra, Samir Bhatt, H. Juliette T. Unwin · 发表于：BMC Public Health · 年份：2025 · DOI：10.1186/s12889-025-22705-4 · 被引用次数：18 · 研究领域：Explainable Artificial Intelligence (XAI)、Advanced Causal Inference Techniques、Machine Learning in Healthcare

BACKGROUND: Understanding the complex interplay between life course exposures, such as adverse childhood experiences and environmental factors, and disease risk is essential for developing effective public health interventions. Traditional epidemiological methods, such as regression models and risk scoring, are limited in their ability to capture the non-linear and temporally dynamic nature of these relationships. Deep learning (DL) and explainable artificial intelligence (XAI) are increasingly applied within healthcare settings to identify influential risk factors and enable personalised interventions. However, significant gaps remain in understanding their utility and limitations, especially for sparse longitudinal life course data and how the influential patterns identified using explainability are linked to underlying causal mechanisms. METHODS: We conducted a controlled simulation study to assess the performance of various state-of-the-art DL architectures including CNNs and (attention-based) RNNs against XGBoost and logistic regression. Input data was simulated to reflect a generic and generalisable scenario with different rules used to generate multiple realistic outcomes based upon epidemiological concepts. Multiple metrics were used to assess model performance in the presence of class imbalance and SHAP values were calculated. RESULTS: We find that DL methods can accurately detect dynamic relationships that baseline linear models and tree-based methods cannot. Howeve...