Research

My work focuses on healthcare and clinical data analytics aimed at understanding health, resilience, and chronic disease through longitudinal patterns, physiological dynamics, and systems-level biological processes.

I am particularly interested in developing analytically and clinically grounded models that move beyond single-variable risk factors toward multi-system, time-dependent representations of human health. This includes the study of electronic health record (EHR) data, wearable-derived metrics, and clinical biomarkers, with emphasis on interpretable approaches that connect quantitative patterns to clinically meaningful phenomena.

Current Research Themes

Longitudinal & Systems-Oriented Clinical Data Analysis

Investigating time-dependent patterns in real-world clinical datasets to better characterize adaptation, risk trajectories, and multi-system interactions in chronic disease.

Clinical Informatics & EHR-Based Analysis

Designing and applying methods for extracting and structuring clinical data — including both structured variables and clinical text — to support retrospective cohort studies, phenotyping, and decision-oriented analytics.

Physiology & Network-Informed Modeling Perspectives

Exploring frameworks that treat physiological systems and organ functions as interacting processes, with the goal of improving how we interpret variability, regulatory dynamics, and resilience across biological systems.

Digital Health & Wearable Data Interpretation

Examining how wearable-derived signals (e.g., heart rate variability, sleep metrics, autonomic indicators) can complement clinical datasets and contribute to multi-modal representations of physiological function.

Systems Perspectives on Metabolic & Chronic Disease

Applying quantitative and systems-oriented analytical approaches to conditions such as type 2 diabetes and metabolic dysregulation, emphasizing longitudinal dynamics and multi-system relationships rather than isolated endpoints.

Ongoing Projects

• Development of privacy-preserving workflows for clinical and EHR-based data analysis
• Longitudinal modeling approaches for metabolic risk and physiological variability
• Integration of wearable-derived metrics with clinical biomarkers

(Additional project details forthcoming.)

Collaboration

welcome collaboration with researchers, clinicians, and organizations working with healthcare data, clinical analytics, or systems-oriented approaches to physiology and disease modeling.

My interests include clinical data analysis, longitudinal modeling, interpretable analytics, and the integration of physiological and digital health datasets. Potential collaborations may involve retrospective EHR studies, cohort-based analyses, digital health data interpretation, or methodological development.

For research or applied analytics collaborations: 📧 drpkalnins@gmail.com