Cardiovascular Data Science
The Cardiovascular Data Science Theme at our school employs five key research units to advance our understanding of cardiovascular diseases and improve patient outcomes:
- Biomarkers Research Unit: Focuses on blood-based biomarkers in observational and intervention studies, revealing insights into cardiometabolic diseases.
- Coding and Analysis Research Unit: Promotes innovation and collaboration across scientific domains, streamlining research through shared data and expertise in diverse coding languages and analysis platforms.
- Healthcare Data Research Unit: Leads national and international efforts using healthcare data from various sources, enhancing patient outcomes by identifying trends and developing predictive models.
- Imaging Data Research Unit: Utilises existing data and advanced techniques to study imaging, deepening understanding of disease mechanisms, improving diagnosis, and predicting patient outcomes.
- Machine Learning Research Unit: Applies AI algorithms to extract patterns from large datasets, enhancing predictive capabilities, identifying risk factors, and optimising treatment strategies in cardiovascular research.
Collectively, these research units form a cohesive and interdisciplinary team dedicated to advancing cardiovascular data science. By integrating biomarker analysis, collaborative coding, healthcare data analysis, imaging studies, and machine learning, our school strives to be at the forefront of innovative research, contributing significantly to the field of cardiovascular medicine and patient care.