Part 2 — Loading SNOMED CT Tables Using Pandas

After exporting the SNOMED CT tables as UTF-8 CSV files, the next step is to load them into Python. Using Pandas provides flexible and memory-efficient handling of large datasets, allowing us to preprocess, filter, and join tables before embedding.

Key Operations

• Read CSV files into DataFrame objects with correct data types.
• Filter active concepts, preferred terms, and valid relationships.
• Join concept, description, and relationship tables as needed.
• Save cleaned datasets for embedding computation.

Efficient data loading and normalization ensure the semantic graph represents valid, interpretable medical relationships before vectorization.