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 32, an i10-index of 58, and over 3,000 citations.

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

Lab's project.

1. Utilizing Large Language Models and Real-World Data to Enhance Medication Safety: Creating RxLlama

The emergence of large language models (LLMs), such as ChatGPT, offers new approaches to improving medication safety due to their powerful data processing, interactive learning, and dialogue capabilities. However, these models largely depend on Internet data and lack access to critical resources like electronic medical records and expert evaluations. Moreover, using cloud-based LLMs poses risks to patient data security, raising concerns for clinical use. This project, titled "Utilizing Large Language Models and Real-World Data to Enhance Medication Safety: Creating RxLlama," aims to leverage the capabilities of open-source LLMs (specifically Llama) to improve prescription safety through advanced prompt engineering and real-world data integration. It will involve model refinement, validation in medical settings, and exploration of educational and clinical applications. By integrating human feedback and external medical databases, we seek to align the model with expert knowledge for continuous improvement.

2. 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.