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Ai Clinical Informatics

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Ai Clinical Informatics

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Research Frontiers in Causal Inference Methods in Healthcare Analytics

Develops causal inference frameworks to distinguish correlation from causation in observational clinical data for evidence-based treatment recommendations.

Causal Discovery in High-Dimensional Electronic Health Records

Instrumental Variables for Unmeasured Confounding in Clinical Trials

Heterogeneous Treatment Effects Across Patient Subpopulations

Causal Inference Under Missing Data in Longitudinal Healthcare

Temporal Causal Dynamics in Disease Progression Networks

Causal Pathways Between Social Determinants and Clinical Outcomes

Mediation Analysis in Complex Drug-Disease Interactions

Counterfactual Reasoning for Personalized Clinical Decision Support

Causal Inference from Real-World Evidence and Registry Data

Graph-Based Causal Models in Multimodal Patient Phenotyping

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