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Related Concept Videos

Investigation of Disease Outbreaks01:23

Investigation of Disease Outbreaks

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Related Experiment Videos

Clustering-based federated causal discovery for multicenter clinical data analysis.

Mingyang Zhang1, Hongnian Wang2, Ju Zhao3

  • 1School of Social Sciences, Henan Normal University, Xinxiang 453007, China.

Journal of Biomedical Informatics
|June 6, 2026
PubMed
Summary
This summary is machine-generated.

Clustering-Based Federated Causal Discovery (CFedCD) improves causal learning in distributed, non-IID clinical data. This framework enhances accuracy by clustering sites and building personalized causal graphs, outperforming standard federated learning.

Keywords:
Acute kidney injuryClustering-based learningData heterogeneityElectronic medical recordsFederated causal learningRisk prediction

Related Experiment Videos

Area of Science:

  • * Causal inference and machine learning
  • * Health informatics and electronic health records
  • * Distributed and federated learning systems

Background:

  • * Traditional causal discovery methods face challenges with distributed, privacy-sensitive, and non-independent and identically distributed (non-IID) data.
  • * Multicenter clinical data analysis requires robust methods that handle data heterogeneity and preserve patient privacy.
  • * Existing federated learning approaches may not fully capture complex causal relationships in diverse patient populations.

Purpose of the Study:

  • * To propose the Clustering-Based Federated Causal Discovery (CFedCD) framework for enhanced causal learning in multicenter clinical settings.
  • * To address limitations of traditional causal discovery algorithms in distributed and privacy-sensitive environments.
  • * To improve the accuracy and applicability of causal discovery using electronic medical record (EMR) data.

Main Methods:

  • * Integrated deterministic representation encoding and federated optimization within the CFedCD framework.
  • * Utilized a Deep Sets model for local extraction of private feature digests from EMR data.
  • * Employed K-means clustering on server-side aggregation to group clients by data characteristics, followed by cluster-specific causal graph construction.

Main Results:

  • * CFedCD identified key candidate causal factors for acute kidney injury (AKI), including pulmonary disease, hypertension, and diabetes.
  • * Demonstrated significant improvements in causal learning and predictive performance (0.014 AUROC increase, p<0.05) compared to baseline federated learning.
  • * Revealed substantial heterogeneity in patient populations and clinical practices through cluster-specific causal graph visualizations.

Conclusions:

  • * The CFedCD framework provides an effective solution for federated causal structure learning in heterogeneous environments.
  • * Generated graphical models representing candidate causal relationships informed by observational data and clinical knowledge.
  • * Offers a privacy-preserving approach to uncover complex causal associations in distributed clinical datasets.