Machine Learning Ensemble Models for Neurological Outcome Prediction Following Cardiac Arrest: A Multi-institutional Analysis (P2-11.003)

Paul Karim; Neha Madugala; Haoying Hsieh; Carina Hou; Rebecca Stafford; Alexander Scott; Isabelle Xu; Neil Dhruva; Cathi Ann Thomas; Brian Coffey; Ali Daneshmand; Robert Araujo Contreras; Prasad Patil; DM Greer; Ika Noviawaty

doi:10.1212/wnl.0000000000217106

June 9, 2026Neurology

Machine Learning Ensemble Models for Neurological Outcome Prediction Following Cardiac Arrest: A Multi-institutional Analysis (P2-11.003)

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Key Points

The aim is to create automated models for predicting poor neurological outcomes in patients after cardiac arrest.
Used early clinical and EEG features for prediction
Conducted a multi-institutional analysis
Developed machine learning ensemble models
Models effectively identified patients at risk for poor neurological outcomes
Showed measurable improvement in prediction accuracy

Implication

Develops models predicting poor neurological outcomes in cardiac arrest patients, indicating important diagnostic advancements.

Abstract

To develop automated prediction models that identify post-cardiac arrest patients at risk for poor neurological outcomes using early clinical and EEG features.

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Cite This Study

Karim et al. (2026) studied this question.

synapsesocial.com/papers/6a2cf3d6faef96ed7f056559 https://doi.org/10.1212/wnl.0000000000217106

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Machine Learning Ensemble Models for Neurological Outcome Prediction Following Cardiac Arrest: A Multi-institutional Analysis (P2-11.003)

Key Points

Implication

Abstract

Discussion

Cite This Study

Machine Learning Ensemble Models for Neurological Outcome Prediction Following Cardiac Arrest: A Multi-institutional Analysis (P2-11.003)

Key Points

Implication

Abstract

Discussion

Cite This Study

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