DBToken: A Database Tokenizer for Medical Event Foundation Models

Objectives Transformer models for electronic health records require converting clinical data into token sequences, however standardized tokenization and evaluation frameworks are lacking. We introduce DBToken, an open-source library, and bits-per-row (BPR), a metric for comparing tokenization strategies. Materials and Methods DBToken accepts Medical Event Data Standard (MEDS)-compatible input and supports multiple text, numeric, and temporal tokenization strategies. BPR extends the bits-per-byte metric used in language models to enable comparison across tokenization strategies. Results DBToken efficiently tokenized data across configurations. BPR identified the vocabulary size associated with the best clinical outcome performance and localized differences in numeric tokenization performance by token class. Discussion Optimal tokenization strategies for medical foundation models are a subject of active research. DBToken enables reproducible tokenization experiments, while BPR efficiently screens vocabulary sizes and numeric representations before downstream evaluation. Conclusion DBToken and the BPR metric provide open-source infrastructure for reproducible EHR tokenization and cross-strategy evaluation.