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Self-Alignment Pretraining for Biomedical Entity Representations
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Despite the widespread success of self-supervised learning via masked language models (MLM), accurately capturing fine-grained semantic relationships in the biomedical domain remains a challenge. This is of paramount importance for entity-level tasks such as entity linking where the ability to model entity relations (especially synonymy) is pivotal. To address this challenge, we propose SapBERT, a pretraining scheme that self-aligns the representation space of biomedical entities. We design a scalable metric learning framework that can leverage UMLS, a massive collection of biomedical ontologies with 4M+ concepts. In contrast with previous pipeline-based hybrid systems, SapBERT offers an elegant one-model-for-all solution to the problem of medical entity linking (MEL), achieving a new state-of-the-art (SOTA) on six MEL benchmarking datasets. In the scientific domain, we achieve SOTA even without task-specific supervision. With substantial improvement over various domain-specific pretrained MLMs such as BioBERT, SciBERTand and PubMedBERT, our pretraining scheme proves to be both effective and robust.
Forward citations
Cited by 8 Pith papers
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A text-to-FHIR pipeline produces a synthetic dataset with 82.5% valid bundles and demonstrates reduced LLM diagnostic accuracy on structured EHR data versus plain text.
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MedCase-Structured: A Text-to-FHIR Dataset for Benchmarking Diagnostic Reasoning in Clinically Realistic EHR Settings
A staged LLM pipeline with terminology validation produces FHIR bundles from text cases at 82.5% validity rate, revealing reduced LLM diagnostic accuracy on structured EHR-like inputs versus plain text.
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MedCase-Structured: A Text-to-FHIR Dataset for Benchmarking Diagnostic Reasoning in Clinically Realistic EHR Settings
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Spherical Mixture Integration for Latent Embedding Alignment across Multi-Source Feature Spaces
SMILE aligns latent embeddings across multi-source EHR feature spaces via spherical mixtures of von Mises-Fisher distributions, provides non-asymptotic error bounds, and enables consistent synonym cluster recovery.
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Spherical Mixture Integration for Latent Embedding Alignment across Multi-Source Feature Spaces
SMILE models synonymy in multi-EHR codes via spherical mixtures of von Mises-Fisher distributions and develops a composite quasi-likelihood estimator with non-asymptotic error bounds and consistent cluster recovery.
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The Provenance Gap in Clinical AI: Evidence-Traceable Temporal Knowledge Graphs for Rare Disease Reasoning
HEG-TKG grounds LLM clinical reasoning in hierarchical evidence-based temporal knowledge graphs from 4,512 PubMed records, delivering 100% citation verifiability and error detectability where standard RAG and unprompt...
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MedGuards: Multi-Agent System for Reliable Medical Error Detection and Correction
MedGuards proposes a multi-agent system for medical error detection and correction plus the KPCS metric, reporting gains on four multilingual clinical-note datasets.
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