arXiv (All)
open access
Are Joint-Embedding Predictive Objectives Effective for Longitudinal EHRs?
Masked language modelling (MLM) is the dominant pretraining objective for transformer encoders applied to longitudinal electronic health records (EHRs). However, reconstructing individual events may overemphasise noisy or weakly informative observations and does not explicitly encourage learning hig
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
- Secure Aggregation for Privacy-Preserving Federated Learning on Clinical EEG Data#984207
- Residual Vector-based Reconstruction as Long-Context Recall Regardless of Context Window Size#1070167
- Forgetting Only What Matters: Layer-Selective Unlearning toward Robust LLMs#997322
- Will the GDPR Restrain Health Data Access Bodies Under the European Health Data Space (EHDS)?#964777
- Persistent Depth Ordering amid Shifting Block-Bypass Responses in Language Model Pretraining#1156100
Record · ID 1414133
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