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. 2025 Jan 15;25(1):27.
doi: 10.1186/s12911-025-02849-4.

Causal analysis for multivariate integrated clinical and environmental exposures data

Meghamala Sinha  1 , Perry Haaland  2 , Ashok Krishnamurthy  3   4 , Bo Lan  5 , Stephen A Ramsey  6 , Patrick L Schmitt  3 , Priya Sharma  3 , Hao Xu  3 , Karamarie Fecho  3
Affiliations

Causal analysis for multivariate integrated clinical and environmental exposures data

Meghamala Sinha et al. BMC Med Inform Decis Mak. .

Erratum in

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.

Keywords: Asthma; Causal inference; Open clinical data; Structure learning.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: A waiver of informed consent for research [45 CFR 46.116(d)] and a waiver of HIPAA authorization [45 CFR 164.512(i)(2)(ii)] were granted by the Institutional Review Board at the University of North Carolina at Chapel Hill (protocol 16-2978). Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Stacked bar chart representing the number of TotalEDInpatientVisits across each level of the feature variables. See Table 1 for feature variable definitions
Fig. 2
Fig. 2
Relative feature importance for all features with respect to TotalEDInpatientVisits. See Table 1 for feature variable definitions
Fig. 3
Fig. 3
Inferred causal graph. Solid black lines represent inferred expected edges based on subject matter expertise combined with published literature (true positives), dashed lines represent missed expected edges (false negatives), and red lines represent unexpected edges, meaning not expected based on subject matter expertise or the published literature (false positives)
Fig. 4
Fig. 4
The change in the mean number (% increase) of TotalEDInpatientVisits after each intervention: a 0.5681 to 0.6642 mean number of visits (9.62% increase) for Obesity; b 0.5681 to 0.7271 mean number of visits (15.90% increase) for Prednisone; and c 0.5681 to 0.5722 mean number of visits (0.42% increase) for Sex. Interv = intervention
See this image and copyright information in PMC

References

    1. Pearl J. Causality: models, reasoning, and inference. Economet Theor. 2003;19(675–685):46.
    1. Pearl J. Causal diagrams for empirical research. Biometrika. 1995;82:669–710. 10.1093/biomet/82.4.669.
    1. Pearl J. Causality: models, reasoning, and inference. 2nd ed. New York: Cambridge University Press; 2000. p. 2009.
    1. Rizzi DA. Causal reasoning and the diagnostic process. Theoret Med. 1994;15(3):315–33. - PubMed
    1. Rubin D. Estimating causal effects of treatments in randomized and non randomized studies. J Educ Psychol. 1974;66:688–701. 10.1037/h0037350.