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Related Experiment Video

Updated: Sep 2, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Machine Learning-Based Prediction Models for Delirium: A Systematic Review and Meta-Analysis.

Qi Xie1, Xinglei Wang2, Juhong Pei3

  • 1School of Nursing, Lanzhou University, Lanzhou, Gansu, China.

Journal of the American Medical Directors Association
|August 3, 2022
PubMed
Summary

Machine learning (ML) models demonstrate excellent performance in predicting delirium, achieving a pooled area under the receiver operating characteristic curve of 0.89. However, current approaches face challenges in comparability and reproducibility, requiring further refinement for clinical deployment.

Keywords:
Machine learning algorithmdeliriummeta-analysispredictive modelsystematic review

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Medicine
  • Clinical Prediction Models

Background:

  • Delirium is a common and serious condition in hospitalized patients.
  • Accurate prediction of delirium is crucial for timely intervention and improved patient outcomes.
  • Existing methods for delirium prediction have limitations.

Purpose of the Study:

  • To critically appraise and quantify the performance of machine learning (ML) models for delirium prediction.
  • To systematically review and meta-analyze studies employing ML in delirium prediction.

Main Methods:

  • Systematic review and meta-analysis of studies using ML for delirium prediction in adult patients.
  • Searched multiple databases (PubMed, Embase, etc.) from inception to December 2021.
  • Extracted data, assessed risk of bias, and performed meta-analysis using Metadisc software.

Main Results:

  • Included 22 studies, with 4 quantitatively analyzed.
  • Pooled performance metrics for ML models: Area Under the Receiver Operating Characteristic Curve (AUROC) = 0.89, Sensitivity = 0.85, Specificity = 0.80.
  • Significant heterogeneity observed in study reporting regarding participants, features, and methodology.

Conclusions:

  • ML models show excellent performance in predicting delirium.
  • Current ML approaches for delirium prediction suffer from low comparability and reproducibility.
  • Recommendations are provided to address challenges and facilitate prospective deployment of ML models.