Public articles linked to the same research event.
PLOS digital health The study presents SAFE-AI, a framework that restricts a large language model to zero-shot entity extraction from emergency medical services charts and then makes determinations through a deterministic rule-based computational graph built from a clinician-defined ontology of clinical guidelines, reporting 97.9% accuracy for detecting epinephrine overdose and 91.6% for detecting delays in epinephrine administration across 300 pediatric out-of-hospital cardiac arrest charts containing 18,402 lines of clinical information, outperforming the compared baseline models.
The study presents SAFE-AI, a framework that restricts a large language model to zero-shot entity extraction from emergency medical services charts and then makes determinations through a deterministic rule-based computational graph built from a clinician-defined ontology of clinical guidelines, reporting 97.9% accuracy for detecting epinephrine overdose and 91.6% for detecting delays in epinephrine administration across 300 pediatric out-of-hospital cardiac arrest charts containing 18,402 lines of clinical information, outperforming the compared baseline models.
The study presents SAFE-AI, a framework that restricts a large language model to zero-shot entity extraction from emergency medical services charts and then makes determinations through a deterministic rule-based computational graph built from a clinician-defined ontology of clinical guidelines, reporting 97.9% accuracy for detecting epinephrine overdose and 91.6% for detecting delays in epinephrine administration across 300 pediatric out-of-hospital cardiac arrest charts containing 18,402 lines of clinical information, outperforming the compared baseline models.
The study presents SAFE-AI, a framework that restricts a large language model to zero-shot entity extraction from emergency medical services charts and then makes determinations through a deterministic rule-based computational graph built from a clinician-defined ontology of clinical guidelines, reporting 97.9% accuracy for detecting epinephrine overdose and 91.6% for detecting delays in epinephrine administration across 300 pediatric out-of-hospital cardiac arrest charts containing 18,402 lines of clinical information, outperforming the compared baseline models.