🩺 Building Embeddings for SNOMED CT

Why SNOMED CT and why vector search

SNOMED CT stands for Systematized Nomenclature of Medicine – Clinical Terms. That alone says a lot, but let us make it simple.

SNOMED is a structured medical terminology originally developed by the College of American Pathologists to standardize how clinical information is recorded and shared.

CT refers to the comprehensive set of clinical concepts covering diseases, findings, procedures, body structures, organisms, substances, and many other aspects of medicine used in patient records.

In short, SNOMED CT is an international, multilingual healthcare terminology designed to make medical data mean the same thing everywhere. It helps systems, countries, and researchers exchange and understand clinical information consistently.

It is built on a concept description relationship model that allows computers to understand not just what a medical term says but what it means and how it connects to other terms. This semantic structure makes SNOMED CT a strong foundation for modern vector search and semantic embeddings, where language and meaning are translated into numerical vectors and compared mathematically.

Introduction

I decided to share our experience building vector search over SNOMED CT, which we have implemented as part of our medical AI systems. For us, it became more than just data. It turned into a common language to communicate ideas, models, and meaning within our project.

Here on my profile and on this site, I plan to publish a series of short, readable introductions that explain key steps in plain language. Each piece will focus on essential concepts and methods you can adapt to build your own SNOMED based vector search system with minimal adjustments.

For readers who want to go deeper, each post will link to a detailed technical article on our project site:

https://arachnet.eu/snomed/embeddings/index.html

Our goal is simple: to make clinical semantics accessible, share practical experience, and help others explore how vector representations can bring meaning to medical language, one concept and one embedding at a time.

Closing

If you are working with clinical data, semantic search, or healthcare AI, follow this series and visit arachnet.eu for implementation details and updates.

We are always open to collaboration, shared research, and practical ideas that move healthcare AI forward.

Let us build the next layer of meaning in medicine together.