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Detecting emergencies in patient portal messages using large language models and knowledge graph-based retrieval-augmented generation

Study on using large language models and a knowledge graph to triage patient messages for emergency care.

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Detecting emergencies in patient portal messages using large language models and knowledge graph-based retrieval-augmented generation

By Siru Liu, Aileen P. Wright, Allison B. McCoy, Sean S Huang, Bryan D. Steitz, Adam WrightJournal of the American Medical Informatics Association
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The study evaluates the effectiveness of four models in detecting emergency messages in patient portals, with a focus on integrating large language models (LLMs) with a knowledge graph.

The results show that the model incorporating a global search within the knowledge graph outperformed other approaches. This research contributes to the development of AI-assisted triage systems for improving patient safety.

Abstract

The study evaluates the effectiveness of four models in detecting emergency messages in patient portals, with a focus on integrating large language models (LLMs) with a knowledge graph. The results show that the model incorporating a global search within the knowledge graph outperformed other approaches. This research contributes to the development of AI-assisted triage systems for improving patient safety.

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emergency message detectionpatient portal messageslarge language modelsknowledge graph retrieval-augmented generationtriage system developmentLarge Language ModelsRetrieval & RAGKnowledge GraphsSemantic Interoperability
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Detecting emergencies in patient portal messages using large language models and knowledge graph-based retrieval-augmented generation | Aramai