Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Transcranial direct current stimulation to improve cognitive function in patients with vascular cognitive impairment: a literature review.

Frontiers in aging neuroscience·2026

Same author

Efficacy and safety of clopidogrel and aspirin initiated within 72 hours after mild ischemic stroke or high-risk TIA by sex: a prespecified subgroup analysis of the INSPIRES trial.

Expert review of cardiovascular therapy·2026

Same author

Clinical and neuroimaging correlates of motoric cognitive risk syndrome in cerebral small vessel disease: a cross-sectional study.

Frontiers in aging neuroscience·2026

Same author

Application of Brain-Computer Interface Technology in Vascular Cognitive Impairment: A Systematic Review.

Brain sciences·2026

Same author

A multi-centre, phase 1a/1b dose escalation and expansion study of the HER2-directed antibody-drug conjugate T-Bren (BL-M07D1) in advanced breast cancer and other solid tumours.

EBioMedicine·2026

Same author

Photoinduced Structural Relaxation in Chiral Copper(l) Iodide Cluster Scintillators for Circularly Polarized Radioluminescence Imaging.

Advanced materials (Deerfield Beach, Fla.)·2026

Search research articles

Related Experiment Video

Updated: Jan 12, 2026

Chronic Implantation of Whole-cortical Electrocorticographic Array in the Common Marmoset

Chronic Implantation of Whole-cortical Electrocorticographic Array in the Common Marmoset

Published on: February 1, 2019

Noncontact Monitoring and AI-Driven Stroke Prediction: National Center for Neurological Disorders-Based Approach

Lan Lan¹, Jia-Wei Luo², Rui Li¹

¹Information Management and Data Center, Beijing Tiantan Hospital Capital Medical University Beijing China.

Health Care Science

|November 3, 2025

Summary

Early stroke detection is crucial, especially in China. This study developed a predictive tool using smart bed and electronic health record data, achieving 92% accuracy in identifying stroke risk.

Keywords:

artificial intelligence echocardiography electronic medical record prediction stroke time series

More Related Videos

Laser Nanosurgery of Cerebellar Axons In Vivo

Laser Nanosurgery of Cerebellar Axons In Vivo

Published on: July 28, 2014

Real-Time Dynamic Navigation System for the Precise Quad-Zygomatic Implant Placement in a Patient with a Severely Atrophic Maxilla

Real-Time Dynamic Navigation System for the Precise Quad-Zygomatic Implant Placement in a Patient with a Severely Atrophic Maxilla

Published on: October 18, 2021

Related Experiment Videos

Last Updated: Jan 12, 2026

Chronic Implantation of Whole-cortical Electrocorticographic Array in the Common Marmoset

Chronic Implantation of Whole-cortical Electrocorticographic Array in the Common Marmoset

Published on: February 1, 2019

Laser Nanosurgery of Cerebellar Axons In Vivo

Laser Nanosurgery of Cerebellar Axons In Vivo

Published on: July 28, 2014

Real-Time Dynamic Navigation System for the Precise Quad-Zygomatic Implant Placement in a Patient with a Severely Atrophic Maxilla

Real-Time Dynamic Navigation System for the Precise Quad-Zygomatic Implant Placement in a Patient with a Severely Atrophic Maxilla

Published on: October 18, 2021

Area of Science:

Neurology
Artificial Intelligence
Biomedical Engineering

Background:

Stroke is a leading cause of death and disability globally, particularly in China.
Low rates of timely intravenous thrombolysis highlight the need for advanced predictive tools.
Elderly individuals living alone are a vulnerable population requiring accessible stroke early warning systems.

Purpose of the Study:

To develop and evaluate a real-time stroke prediction model.
To integrate data from nonintrusive smart beds and electronic medical records for enhanced prediction.
To provide early warnings for potential strokes, especially for at-risk elderly populations.

Main Methods:

Collected continuous smart bed monitoring data and multimodal temporal electronic medical record data.
Applied feature engineering, selection, and machine learning models, including deep learning for temporal data.
Evaluated model performance using Area Under the Receiver Operating Characteristic Curve (AUROC) and accuracy.

Main Results:

The random forest model, incorporating all features, achieved an AUROC of 0.94 and 92% accuracy.
Smart bed features alone provided a moderate prediction capability (AUROC 0.59-0.63, accuracy 60%-65%).
Integration of multimodal temporal data significantly improved stroke prediction accuracy.

Conclusions:

Multimodal temporal data integration successfully identified stroke occurrence.
Noncontact monitoring of vital signs via smart beds shows promise for home-based stroke surveillance.
This approach is particularly beneficial for elderly individuals living alone, offering a pathway to timely intervention.