SNOMED CT
PulseAugur coverage of SNOMED CT — every cluster mentioning SNOMED CT across labs, papers, and developer communities, ranked by signal.
2 day(s) with sentiment data
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LLM judges show bias in entity alignment evaluations, new study finds
A new study published on arXiv investigates the reliability of large language models (LLMs) when used as judges to evaluate entity alignment (EA) across knowledge graphs. Researchers found that LLMs exhibit significant …
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Edge-deployable AI structures radiology reports with small language model
Researchers have developed MedPAO-Assist, an agentic AI framework designed for structuring radiology reports directly from radiologist dictation. This system is notable for its edge-deployability, requiring minimal reso…
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LLM judges show anchor bias in entity alignment evaluations
A new study by Vaibhav Alakshmi Ravideshik explores the reliability of using large language models (LLMs) as judges for evaluating entity alignment (EA) systems. The research found that LLM judges can exhibit "anchor bi…
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New ontology aims to unify crisis management knowledge representation
Researchers have developed the European Crisis Management Ontology (ECMO), a modular OWL-based ontology designed for cross-sectoral crisis management and disaster risk reduction. ECMO-CORE establishes fundamental concep…
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New benchmark improves SNOMED CT concept recommendation from clinical text
Researchers have developed a new benchmark for recommending SNOMED CT concepts from masked clinical contexts, aiming to improve the standardization and interoperability of clinical language. The study utilized the SNOME…
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New Relational Hypergraph Transformer Enhances Multi-Table Learning
Researchers have introduced the Relational Hypergraph Transformer (RHT), a novel architecture designed to address the complexities of multi-table learning, particularly in healthcare data. The RHT represents relational …
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UK develops national EHR generative AI model for pandemic prediction
A new research paper details the development of Foresight-England, a large-scale generative AI model designed to predict medical events using electronic health records (EHRs) specifically within the context of the COVID…
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New Moose method enhances neuro-symbolic learning for OWL 2 EL ontologies
Researchers have developed Moose, a novel neuro-symbolic learning method designed for the OWL 2 EL profile, which is utilized in large-scale ontologies like Gene Ontology and SNOMED CT. Unlike previous methods that hand…
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LLMs struggle to apply CBT principles despite theoretical knowledge
A new research paper explores the limitations of Large Language Models (LLMs) in applying Cognitive Behavioral Therapy (CBT) principles to affective reasoning. While LLMs demonstrate theoretical knowledge of CBT, they s…
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Machine learning aids cardiology EHR terminology extraction
Researchers have developed a machine learning technique to create a Cardiology Interface Terminology (CIT) for better highlighting of details within electronic health records (EHRs). This method involves a three-phase p…
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Schema-Grounded LLM Extracts Patient Data for Digital Twins
Researchers have developed SG-LLM, a novel method for extracting patient data from electronic health records to create digital twins. This approach grounds LLM extraction with schema constraints and a validation loop fo…
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New architecture links radiology report findings to evidence
Researchers have proposed a new reference architecture for structured radiology reporting that links evidence directly to report content. This human-supervised system aims to extract and organize structured information,…
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LongBEL framework improves biomedical entity linking with document context
Researchers have developed LongBEL, a new framework for biomedical entity linking that considers the entire document context rather than just individual mentions or sentences. This approach aims to improve consistency b…
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New MCP server integrates 7 medical terminologies for LLMs
A new Model Context Protocol (MCP) server called medical-terminologies-mcp has been released, providing unified access to seven major medical terminology systems. This tool is designed to help Large Language Models (LLM…
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Neuro-symbolic AI advances offer explainability and reasoning beyond pure neural networks
Researchers are developing neuro-symbolic AI models that combine neural networks with symbolic reasoning to improve explainability and performance. Gyan, a novel non-transformer architecture, aims to overcome limitation…