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Related Concept Videos

Dementia01:30

Dementia

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Dementia is a collective term for cognitive disorders primarily affecting memory, thinking, and reasoning. It is not a specific disease but a syndrome, with Alzheimer's disease being the most common cause, accounting for approximately 60-80% of cases. Other types include vascular dementia, Lewy body dementia, and frontotemporal dementia. Dementia affects millions worldwide, particularly older adults, though it is not a normal part of aging.
The progression of dementia is generally gradual....
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Alzheimer's Disease (AD) is a continually advancing neurodegenerative disorder, distinguished by escalating memory loss, cognitive dysfunction, and dementia. The disease unfolds in three stages: preclinical, mild cognitive impairment (MCI), and dementia. Its onset is insidious, and the progression gradual, with the cause not well explained by other disorders.
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Related Experiment Video

Updated: May 23, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Comprehensive Machine Learning-Based Prediction Model for Delirium Risk in Older Patients with Dementia: Risk Factors

Qifan Xiao1, Shirui Zhou2, Bin Tang1

  • 1General Practice, International Department, China-Japan Friendship Hospital, Beijing, People's Republic of China.

Clinical Interventions in Aging
|May 21, 2025
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Summary

Machine learning accurately predicts delirium risk in dementia patients. Key factors like cerebrovascular disease and sedative use were identified for early intervention.

Keywords:
deliriumdementiamachine learningolderpredictionrisk factors

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Area of Science:

  • Gerontology
  • Neurology
  • Artificial Intelligence

Background:

  • Delirium superimposed on dementia (DSD) is a severe complication in older adults with dementia.
  • It presents with fluctuating cognition, inattention, and altered consciousness, complicating diagnosis.
  • DSD increases cognitive decline, hospitalization duration, and mortality risk.

Purpose of the Study:

  • To develop a machine learning (ML) model for predicting delirium risk in older dementia patients.
  • To identify significant risk factors for DSD to aid clinical decision-making.
  • To improve early intervention strategies for DSD.

Main Methods:

  • Prospective data collection from 636 older dementia patients.
  • Development and comparison of five ML algorithms: XGBoost, Random Forest, MLP, Categorical Boosting, and Logistic Regression.
  • Feature importance analysis using SHAP to identify key risk factors.

Main Results:

  • The Extreme Gradient Boosting (XGB) model achieved the highest predictive performance (AUC 0.930, Accuracy 0.870).
  • Significant risk factors identified include cerebrovascular disease, sedative use, low hemoglobin, high VAS score, diabetes, and hypertension.
  • A compact XGB model using the top 10 variables also showed strong predictive capability.

Conclusions:

  • A robust ML-based prediction model for DSD risk was successfully developed.
  • The XGB model offers high accuracy in identifying patients at risk of delirium.
  • Identified risk factors can guide targeted interventions to improve DSD management in clinical settings.