I am Hsuan-Chia Yang, currently serving as an Associate Professor at the Graduate Institute of Biomedical Informatics, Taipei Medical University, Taiwan. My research focuses on leveraging medical big data, clinical decision support systems, and pharmacoepidemiology to improve healthcare outcomes. With a background in pharmacy, I specialize in applying artificial intelligence to reduce medication errors and enhance drug safety. My work also delves into exploring the links between long-term drug use and cancer, aiming to develop effective chemoprevention strategies. To date, I have published over 100 academic papers, with an H-index of 38, an i10-index of 64, and over 4,000 citations.

https://scholar.google.com.tw/citations?user=YeNALrEAAAAJ&hl=zh-TW

Lab's project.

1. To create individual risk matrix between long-term drug and cancer

Cancer has increasingly been recognized as a chronic disease, with its treatment accounting for 13% of Taiwan's total health insurance expenses. Effective cancer treatment planning and benefit assessments should include preventive strategies beyond traditional therapeutic approaches. Chemoprevention, the use of medications to reduce cancer risk, is a well-established concept. Epidemiological studies have shown that long-term use of certain chronic disease medications is significantly linked to a lower risk of specific cancers. To analyze the relationship between chronic medication use and cancer risk, we must consider factors such as drug types, cancer categories, age, and gender, utilizing multiple analytical approaches. This complex issue cannot be addressed with a single method, but it requires leveraging medical informatics. By utilizing big medical data and applying data mining techniques, we can systematically assess the associations between the long-term use of chronic medications and cancer risk. Visualization techniques will help make these insights accessible, enabling the development of personalized risk matrices.

https://doi.org/10.7150/jca.125694

https://doi.org/10.3390/cancers17101616

https://doi.org/10.1111/cas.16422

https://doi.org/10.3390/ijms24043814

2. To Develop an EHR-Based Cancer Risk Prediction Framework

Cancer is a major global health burden, and early identification of individuals at high risk is essential for effective prevention and timely intervention. With the widespread adoption of electronic health records (EHRs), large-scale real-world clinical data have become available, offering unprecedented opportunities for predictive modeling in oncology.

This project aims to develop an interpretable cancer risk prediction framework using longitudinal EHR data, primarily leveraging disease diagnoses, medication exposure, and laboratory test results. By modeling the complex interactions and temporal patterns among these three core clinical data domains, we will construct machine learning models to identify individuals at elevated cancer risk before clinical diagnosis.

https://doi.org/10.2196/26256

https://doi.org/10.2196/19812