Prof. Hsiu-Hsi Chen is an expert in evaluation of intervention program, with emphasis on population-based cancer screening and also universal vaccination program, by using a series of sophisticate statistical modelling to deal with several thorny issues that cannot be solved by classical approaches. These include the development of different health economic decision models for cancer screening program and also vaccination program and prophylactic treatment. The recent publications include the evaluation of Taiwan population-based screening in colorectal cancer (Gut), breast cancer (JAMA Oncology), and oral cancer (Cancer). The statistical publications on the methodology of applying stochastic process to evaluation of cancer screening model published in JASA, Biometrics, Statistics in Medicine, and Statistical Methods in Medical Research with Bayesian and non-Bayesian approach have facilitated the development of health economic decision models. A series of original articles cost-effectiveness (benefit) analysis based on these models have been published in international peer review articles.
Regarding international academic cooperation, Professor Chen has taken the presidency of the International Asian Cancer Screening Conference Network held annually since 2004. As far as collaborative research in western countries is concerned, the long-lasting collaboration with Sweden (Falun Central Hospital), the USA (American Cancer Society), United Kingdom (Wolfson Institute of Preventive Medicine), and Finland (School of Public Health, University of Tampere) where Professor Chen was awarded the Finland Distinguished Professor (FIDIPRO) issued by the Academy of Finland between 2007 and 2009. Professor Chen is also involved in the monograph with the International Agency for Research on Cancer (IARC) on oral cancer prevention. A recent report, published in the New England Journal of Medicine, provides a brief summary of this seminal work.
22 MARCH
| Time |
Session |
|
|
14:00
15:30
|
-
-
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.
-
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
|
