Unveiling Hidden Patterns in Patient Data with Medical Embeddings and Clustering

Leveraging Medical Embeddings to Uncover Patterns in Patient Data: A Journey from High-Dimensional Text to Insightful Visual Clusters. Image generated with DALL-E.

Introduction: Bridging the Gap Between Data and Insights

In today’s data-driven healthcare environment, understanding patient similarities and differences is crucial for personalized treatment plans, risk stratification, and early diagnosis. But how can we unlock meaningful insights from the vast complexity of medical histories?

Enter embedding models — powerful tools that transform textual medical records into dense, numerical representations. These embeddings capture the subtle nuances in patient data, enabling advanced analysis and visualization. In this project, I leveraged a pre-trained medical BERT model to group patients based on their medical profiles and visualized their similarities using t-SNE.

The results? A clear demonstration of how embedding models can separate populations (e.g., young healthy vs. old unhealthy patients) and highlight the value of modern AI in healthcare applications.

Methods: From Data to Discovery

The project consists of four key steps:

1. Generating Synthetic Patient Data
2. Embedding Patient Records Using Bio_ClinicalBERT
3. Visualizing Clusters…