Abstract Details

Title Neuro-Copilot AI: Advanced LLM Framework for Neurological Patients in the Emergency Room

Topic General Neurology

Presentation(s) S27 - General Neurology 2 (1:36 PM-1:48 PM)

Poster/Presentation
Number 004

Objective Generating language based neurological framework to identify neurologically high-risk patients in acute care settings.

Background Neurological decisions in the ED impacts healthcare delivery, increases morbidity and imposes economic burden due to inefficient resource utilization and increased treatment expenses. The increase prevalence of neurological disorders as well as the shortage of neurologists accentuate the need for a solution.

Design/Methods We developed large language model (LLM) framework augmented by prompt engineering, retrieval-augmented generation (RAG) based on the historical cases, XGBoost and logistic regression. We then tested the model using consecutive patients who received neurological consultations in the ED (n=1368). Primary endpoints were admission and mortality. Results were also tested with blinded senior neurologists unaware to the actual decision and the LLM outputs.

Results We included 1368 patients in 2 months period, median age of 58.6 [38.37-74.5], 48.46% were males, 45.68% admitted. There was no significant demographical or racial bias towards admission or discharge, we noted higher rate of night shift consultations among admitted patients (36% vs. 18.7%, p < 0.001). The Neuro-Copilot AI framework achieved an AUC of 0.91 for predicting general admission, an AUC of 0.92 for predicting admission to neurological department, an AUC of 0.92 for long-term mortality risk, and 0.96 for 48-hour mortality risk. We used 3 blinded experts for validation. The Fleiss' kappa for admission was only 0.21, reflecting the inherent subjectivity in clinical admission decisions. However, Neuro-Copilot AI predictions showed a strong correlation with the average expert score (Pearson correlation 0.79, p < 0.001)

Conclusions We demonstrate LLM based classifier effectively identifies high-risk patients and provides highly accurate predictions of 48-hour mortality. This represents a crucial first step towards the integration of such models into the fast-paced environment of emergency departments.

Authors/Disclosures
Shahar Shelly, MD (Rambam Medical Center) PRESENTER	Dr. Shelly has stock in Remepy.
Alon Gorenshtein	Mr. Gorenshtein has nothing to disclose.
Shiri Fistel, Msc	Mrs. Fistel has nothing to disclose.
Moran Sorka, PhD	Mrs. Sorka has nothing to disclose.
Gregory J. Telman (Rambam Med Center)	No disclosure on file
Raz Winer, MD	Dr. Winer has nothing to disclose.
Shlomi Peretz, MD	Dr. Peretz has nothing to disclose.
Dvir Aran, PhD	Dr. Aran has received personal compensation in the range of $50,000-$99,999 for serving as a Consultant for Elevance Heatlh. Dr. Aran has received personal compensation in the range of $10,000-$49,999 for serving as a Consultant for Link Cell Therapies.

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