AI Based Clinical Decision-Making Tool for Neurologists in the Emergency Department.

ABSTRACT: Introduction: We aimed to prove integration of advanced machine learning methods within a robust ensemble framework can enhance clinical decision-support for neurologists managing patients in the emergency department (ED). Methods: We engineered an ensemble framework leveraging the capabilities of the Gemini 1.5-pro-002 large language model (LLM). The model was enhanced using prompt engineering and retrieval-augmented generation (RAG). Predictive modeling achieved by combining eXtreme Gradient Boosting (XGBoost) and logistic regression for optimal accuracy in clinical decision-making. Key clinical outcomes, such as admission and mortality, were assessed. A random subset of 100 cases was reviewed by three senior neurologists to evaluate the alignment of the AI's predictions wi

SUBMITTER: Gorenshtein A 

PROVIDER: S-EPMC12429661 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature