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Yale Researchers Use Large Language Models to Detect Gastrointestinal Bleeding

A team of Yale researchers demonstrated the effectiveness of a large language model (LLM)-based pipeline in identifying overt gastrointestinal bleeding in 1,108 patients seen between 2014 and 2023 in the Yale-New Haven Health System. The study showed a high accuracy of the model in detecting melena, hematochezia, and hematemesis, identifying recurrent bleeding, and catching more than 98% of cases. The algorithm also increased the average per-patient reimbursement.

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