arXiv (OAI Expanded)
open access
Representation Before Training: A Practical Benchmark for Generative Medical Event Model Tokenization
Generative medical event models use tokenized sequences of patient timelines as input, but practical guidance on the many decisions around tokenization is limited. We benchmark quantization granularity, reference-range anchoring, code--value fusion, numeric and temporal encodings, and native versus
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Large Language Model-Guided Evolutionary Discovery of Native Neural Architectures for Spiking Sequence Modeling#1154519
- Knowledge-Enriched Structured EHR Features for 30-Day Hospital Readmission Prediction on MIMIC-IV#1109684
- Representing Temporal Organization in Molecular Biology: A Structuralist Approach#98069
- ClinicRealm: Re-evaluating large language models with conventional machine learning for non-generative clinical prediction tasks.#14927
- Tokenizing single-cell transcriptomes as a native language for large language models#440223
Record · ID 1149436
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive