Respiratory tract infections (RTIs), including pneumonia and bronchitis, are common illnesses that significantly
impact global health, posing substantial challenges to healthcare systems. This research addresses the need for a comprehensive understanding of RTI dynamics and the underlying patterns which link an individual’s early-life exposures, such as medical conditions and adverse childhood experiences (ACEs), with an increased susceptibility to RTIs. Traditional approaches cannot reflect the complexity of longitudinal data, struggling to capture the dynamic interplay of numerous factors influencing health outcomes. In contrast, state-of-the-art machine learning (ML) models have achieved excellent performance in pattern recognition tasks. A simulation study was carried out that employs synthetic longitudinal data, to assess the applicability of several ML models, including ResNet and the Signature method, within life course research. This simulation provides a controlled environment to rigorously test model performance. The ML models demonstrate efficacy in capturing intricate temporal patterns, prompting an investigation of the DANLIFE cohort; a longitudinal data set comprised of Danish registry data. Suboptimal performance in predicting RTIs led to an exploration of other health outcomes, finding that ResNet excelled in predicting diabetes and neurological conditions, revealing the model’s capacity to discern patterns and dependencies in the data. Explainability methods, such as permutation feature importance and SHAP, were used to identify potential risk factors influencing health outcomes, finding a link between incidents of parental alcohol abuse and diabetes. Explainability methods enhance transparency, allowing researchers to interpret and trust the relationships uncovered by ML models. In summary, this study not only leverages innovative ML models but redefines how life course analysis can be approached. Through methodological innovations, it opens avenues for more precise, interpretable, and impactful investigations into the complex dynamics of health trajectories, contributing to the evolution of epidemiological research.