AI in Medicine

As a cardiac surgeon and data scientist, I explore the intersection of clinical medicine and artificial intelligence. My work focuses on leveraging machine learning to improve patient outcomes and advance biomedical research.

Key Areas of Interest

Machine Learning in Biomedicine

I advocate for and apply machine learning algorithms to complex medical datasets. My goal is to develop predictive models that can assist in:

• Risk Stratification: Better predicting surgical risks and outcomes.
• Diagnosis: Enhancing diagnostic accuracy through pattern recognition.

Genomic Data Science

With an MSc in Genomic Data Science, I use computational methods to analyze high-dimensional genomic data. This involves identifying genetic markers associated with congenital heart diseases and other cardiovascular conditions to pave the way for precision medicine.

Reproducible Biostatistics

Transparency is key in medical research. I promote reproducible research practices by using code-based analysis (R, Python) instead of point-and-click software. This ensures that analyses can be audited, repeated, and built upon by the scientific community.

Tools & Technologies

• Languages: R, Python
• Focus: Predictive Modeling, Biostatistics, Data Visualization