Written by Ayesha Sahar, Research Associate, Artificial intelligence and data science in multiple long-term conditions theme.

In recent months, I have focused on developing models to predict patterns and uncover relationships in large datasets, particularly in healthcare. For example, understanding why certain health conditions often co-occur or how long-term medication prescriptions affect patients over time can be challenging. Data modeling provides a solution by analyzing historical data to identify and predict these connections. 

One method I have explored is topic modeling, which organizes complex datasets into groups or “topics.” In healthcare, this might mean identifying clusters of related conditions—such as diabetes and hypertension frequently occurring together—or grouping treatments and prescriptions commonly used for managing these conditions. This helps researchers and clinicians make informed decisions, whether tailoring treatment plans for individual patients or designing studies to explore new healthcare solutions. 

To enhance these insights, I have also employed Principal Component Analysis (PCA). PCA reduces the complexity of large datasets by identifying clusters of related variables (or components) that explain the most variance in the data. This approach simplifies the relationships between numerous conditions and prescriptions, making the data more manageable and meaningful. 

However, raw PCA results can be difficult to interpret. That’s where varimax rotation plays a critical role. This mathematical technique adjusts PCA components to make them more distinct and interpretable. Instead of overlapping clusters, varimax rotation sharpens the focus, clearly linking specific conditions to corresponding treatments. 

By combining these methods, my work simplifies the complexity of healthcare data, transforming it into actionable insights. These models not only reveal patterns but also support better clinical decisions, enabling healthcare providers to design effective, patient-centered care pathways. As we refine these approaches, the potential to improve both research and patient outcomes grows exponentially.