I am a physician scientist working in the fields of endocrinology, metabolism, and adipocyte biology. I currently serve as Chief of the Division of Endocrinology and Metabolism at Tri Service General Hospital, a responsibility I took on in February 2026. Since 2023, I have also had the privilege of directing the Metabolic Syndrome Prevention & Care Center, where I work with a dedicated team to improve metabolic health and patient care.
I received my M.D. from the National Defense Medical Center in 2006 and later pursued my Ph.D. in Medical Sciences at the University of Oxford, completing it in 2018. After returning to Taiwan, I joined Tri Service General Hospital as an attending physician and have continued to grow in both clinical and academic roles. In 2024, I was appointed Associate Professor in the Department of Medicine at the National Defense Medical University, a role that allows me to contribute to medical education while learning continuously from colleagues and students.
My research focuses on adipocyte biology, obesity, fat distribution, and the genetic and epigenetic mechanisms underlying metabolic diseases. I have been fortunate to work with excellent collaborators on studies exploring the role of long non coding RNAs such as HOTAIR in adipogenesis, fat distribution, and cancer biology. Our findings have been published in journals including Cell Reports, Molecular Metabolism, Cancer Cell International, and iScience.
Across my clinical, research, and teaching work, I strive to advance precision medicine in endocrinology and to provide better care for patients with metabolic disorders. I remain grateful for the opportunities to learn, collaborate, and contribute to this field.
22 MARCH
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14:00
15:30
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I-Wen WuTaiwan
Speaker
AI-Assisted Discovery of Omic Signature in Diabetic Kidney DiseaseDiabetic kidney disease (DKD) is a leading cause of chronic kidney disease and end-stage renal disease, posing significant global health and economic burdens. Traditional management of DKD relies on standardized approaches, which often fail to account for the complexity of individual patient profiles.
Precision medicine leverages individualized patient data—spanning genetic, proteomic, metabolic, and clinical information—to optimize diagnosis, risk assessment, and therapeutic interventions. However, translating this paradigm into clinical practice presents significant challenges and opportunities.
This presentation focuses on the practical aspects of integrating precision medicine into DKD management. Key themes include the role of genetic and epigenetic biomarkers in risk stratification, the integration of multi-omics data with machine learning for predictive modeling and the design of personalized treatment regimens using tools such as pharmacogenomics.
By examining real-world implementation strategies and overcoming barriers, this presentation aims to guide healthcare providers, researchers, and policymakers toward a sustainable and patient-centered precision medicine framework for DKD.
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Dee PeiTaiwan
Speaker
The roles of Machine Learning in Medical ResearchArtificial intelligence (AI) is fundamentally transforming clinical research by democratizing access to advanced analytical tools and establishing new standards for scientific publication. This presentation outlines a comprehensive framework for integrating machine learning (ML) methodologies into clinical studies—from raw data preparation to manuscript submission—while addressing critical challenges in model development, validation, and interpretation. We emphasize that financial barriers to sophisticated analysis have largely dissolved with the advent of open-source AI platforms, enabling researchers to move beyond legacy statistical software toward reproducible, transparent ML pipelines.
The workflow begins with strategic data preparation, including appropriate imputation techniques (k-NN, MissForest, MICE) and feature standardization. For binary classification tasks—common in clinical prediction—It is advocate a rigorous protocol encompassing stratified cross-validation, hyperparameter tuning via nested CV, and explicit overfitting controls (regularization, feature limitation to ≥10 events per variable). Model selection should prioritize algorithms matching the clinical question: logistic regression for interpretability, random forests for robust baselines, and gradient boosting for maximal performance—while acknowledging trade-offs in complexity and calibration risk.
Evaluation must extend beyond conventional ROC-AUC to include precision-recall curves (especially for imbalanced data), calibration assessment (slope ~1, intercept ~0), Brier score, and decision curve analysis for clinical utility. SHAP values provide essential interpretability for "black-box" models, translating complex predictions into clinically actionable insights. Crucially, it is stressed that accuracy alone is misleading in medical contexts; minimizing false negatives often carries greater clinical consequence than overall accuracy.
Reproducibility demands fixed random seeds, complete pipeline documentation, and packaging preprocessing steps with final models for deployment. As journals increasingly expect ML-enhanced analyses, studies relying solely on traditional statistics face diminished publication prospects. It is concluded that AI integration is no longer optional but essential for contemporary clinical research seeking impact, rigor, and real-world applicability in an era where algorithmic insight complements—not replaces—clinical expertise.
3F Banquet Hall
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