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Related Experiment Video

Updated: Jun 29, 2026

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
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Prediction of Delirium Risk in Mild Cognitive Impairment Using Time-Series Data, Machine Learning and Comorbidity

Santhakumar Ramamoorthy, Priya Rani, Glenn Matthews

    IEEE Journal of Biomedical and Health Informatics
    |December 8, 2025
    PubMed
    Summary
    This summary is machine-generated.

    Patients with mild cognitive impairment (MCI) face higher delirium risks and mortality. Machine learning models effectively predict delirium in MCI patients by analyzing comorbidities, improving early detection and care.

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    Area of Science:

    • Clinical Medicine
    • Medical Informatics
    • Gerontology

    Background:

    • Delirium is a serious condition with high morbidity and mortality, especially in patients with mild cognitive impairment (MCI).
    • Understanding risk factors and developing predictive models are crucial for managing delirium in vulnerable populations.

    Purpose of the Study:

    • To investigate risk factors for delirium in patients with mild cognitive impairment (MCI).
    • To develop a longitudinal predictive model for delirium using machine learning (ML).

    Main Methods:

    • Retrospective analysis of the MIMIC-IV v2.2 database.
    • Examination of comorbidity patterns and survival probabilities using Kaplan-Meier analysis.
    • Implementation of a Long Short-Term Memory (LSTM) model for predictive modeling.

    Main Results:

    • Distinct comorbidity-associated risk profiles were identified for the MCI population.
    • MCI patients with delirium showed significantly reduced survival probabilities.
    • The LSTM model achieved high predictive accuracy (AUROC=0.92, AUPRC=0.91).

    Conclusions:

    • Comorbidities play a critical role in assessing delirium risk in MCI patients.
    • Time-series predictive modeling, specifically LSTM, is effective in identifying high-risk individuals.
    • These findings can inform clinical strategies for delirium prevention and management in MCI cohorts.