Year: 2025
Type: article
Study design: Observational Study
Abstract: 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.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,990
Awards: G2118698080, Biomedical Data Translator Technical Feasibility Assessment and Architecture Design, Biomedical Data Translator Development of Autonomous Relay Agent: ARAX, North Carolina Translational and Clinical Science Institute (NC TraCS), ICEES+ Knowledge Provider: Leveraging Open Clinical and Environmental Data to Accelerate and Drive Innovation in Translational Research and Clinical Care., North Carolina Translational and Clinical Sciences Institute (NC TraCS)
OpenAlex ID: W4406383170