Electronic health records (EHRs) provide a rich source of observational patient data that can be explored to infer underlying causal relationships. These causal relationships can be applied to augment medical decision-making or suggest hypotheses for healthcare research. In this study, we explored a large-scale EHR dataset on patients with asthma or related conditions (N = 14,937). The dataset included integrated data on features representing demographic factors, clinical measures, and environmental exposures. The data were accessed via a service named the Integrated Clinical and Environmental Service (ICEES). We estimated underlying causal relationships from the data to identify significant predictors of asthma attacks. We also performed simulated interventions on the inferred causal network to detect the causal effects, in terms of shifts in probability distribution for asthma attacks.
Keywords: Asthma; Causal inference; Open clinical data; Structure learning.
© 2025. The Author(s).