Research Focus

We study how clinical biomarkers interact as a system rather than as isolated variables.
After performing over 1,000 surgeries per year as a urologist, our research has shifted toward understanding clinical data through network-based and discrete mathematical approaches.
Our work focuses on testosterone, aging, inflammation, and renal function as interconnected systems that define clinical phenotypes.

Current Research

This section outlines our ongoing research projects. Information regarding clinical study disclosures and opt-out procedures is also provided.

Vision

2026.03.22

Clinical Practice

2026.03.22

Testosterone and Frailty

2026.03.22

Research Background

This section provides a clear and accessible overview of the research conducted in this laboratory, including our conceptual approach to disease and its scientific significance.

1. 

   Mapping Triadic Motifs in Urology

   Identifying reproducible three-symptom network structures across complex urological and urogynecological phenotypes using high-order discrete mathematics and clinical data.

2. 

   Data-Driven Optimized Laser Therapeutic Design

   Utilizing high-volume surgical data and longitudinal outcomes to computationally optimize non-ablative Er:YAG laser therapy pathways for personalized medicine.

3. 

   Testosterone Within Systemic Aging Networks

   Redefining testosterone deficiency as a coordinated, data-driven biological state characterized by non-linear interactions with systemic inflammation and renal function.

4. Decoding Patient Narratives Using NLP

   Applying natural language processing and graph theory to extract structured clinical information from large-scale patient-generated text in real-world settings.

5. System-Level Approach to Functional Frailty

   Understanding frailty not as a collection of symptoms, but as a dynamic reorganization of a multi-domain structured biomarker network.

6. Redefining Medicine as Structured Experience

   Establishing a powerful interface between quantitative clinical precision and the rich spectrum of qualitative patient experience through structural transformation modeling.