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Related Concept Videos

Nursing Clinical Information System01:27

Nursing Clinical Information System

Nursing Clinical Information System (NCIS)
A Nursing Clinical Information System (NCIS) is a specialized type of healthcare information system tailored to meet the unique needs of nursing practice. It incorporates the principles of nursing informatics to streamline information management and improve the quality of care delivery.
Critical attributes of NCIS include:
Ischemic Stroke l: Introduction01:15

Ischemic Stroke l: Introduction

Ischemic stroke is an acute cerebrovascular condition in which blood flow to a brain region is suddenly interrupted, leading to tissue infarction. Neurons depend on continuous oxygen and glucose supply, so even brief reductions in perfusion cause energy failure, ionic imbalance, and irreversible injury. Ischemic strokes are classified into thrombotic and embolic types based on their underlying mechanisms.Thrombotic MechanismsThrombotic stroke develops when a clot forms within a cerebral artery.
Ischemic Stroke ll: Pathophysiology01:15

Ischemic Stroke ll: Pathophysiology

An ischemic stroke occurs when a cerebral blood vessel becomes obstructed, most often by a thrombus or embolus, interrupting the delivery of oxygen and glucose to brain tissue. Because neurons rely on continuous aerobic metabolism, energy failure begins within minutes of reduced perfusion. The region receiving the least blood flow becomes the infarct core, an area of irreversible cellular death. Surrounding this core lies the penumbra, a zone of hypoperfused but still viable tissue that is...

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Related Experiment Video

Updated: Jul 17, 2026

Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide
09:52

Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide

Published on: January 15, 2017

An explainable artificial intelligence framework for clinical decision support in stroke discharge planning.

Seifollah Gholampour1, Arshia Dehghan1, Evelyn B Voura2,3

  • 1Department of Neurological Surgery, University of Chicago Medicine, Chicago, Illinois, United States of America.

Plos One
|July 15, 2026
PubMed
Summary

An explainable AI framework accurately predicts stroke patient discharge destinations, identifying key factors like age and NIHSS score to aid care planning and improve patient outcomes.

Related Experiment Videos

Last Updated: Jul 17, 2026

Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide
09:52

Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide

Published on: January 15, 2017

Area of Science:

  • Artificial Intelligence in Medicine
  • Clinical Informatics
  • Neurology

Background:

  • Stroke is a major cause of death and disability worldwide.
  • Accurate prediction of patient discharge disposition is crucial for effective care planning.
  • Current methods for predicting stroke outcomes require enhancement.

Purpose of the Study:

  • To develop an explainable artificial intelligence (AI) framework for predicting stroke patient discharge categories.
  • To identify key predictors influencing discharge disposition.
  • To enhance early care planning and resource allocation for stroke survivors.

Main Methods:

  • A retrospective study of 1,731 patients with stroke or TIA.
  • Utilized 20 routinely collected electronic health record variables.
  • Employed 10 classifiers with 5-fold cross-validation and SHAP for model interpretation.

Main Results:

  • A multilayer perceptron (MLP) model demonstrated superior performance.
  • The MLP achieved an accuracy of 0.646 and macro-F1 score of 0.548 on the test set.
  • Key predictors identified include NIHSS, length of stay, age (threshold 72.0 years), and primary diagnosis.

Conclusions:

  • An interpretable AI framework successfully identified clinically relevant predictors of stroke discharge.
  • Findings suggest potential for AI in clinical decision support for stroke care.
  • Prospective multicenter validation is necessary for clinical implementation and assessing health-system impact.