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Updated: Jun 19, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Adopting machine learning to predict ICU delirium.

Ali Haider Bangash¹, Bipin Chaurasia²

¹Department of Neurocritical Care, Hhaider 5 Research Group, Rawalpindi, Pakistan.

Neurosurgical Review

|July 26, 2024

Summary

This study explores using automated Machine Learning (ML) to predict Intensive Care Unit (ICU) delirium in severe COVID-19 patients. Early ML-driven predictions can aid clinicians in managing neuropsychiatric complications and reducing patient morbidity.

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Last Updated: Jun 19, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

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Published on: May 15, 2020

Area of Science:

Neuroscience
Artificial Intelligence
Critical Care Medicine

Background:

Neuropsychiatric complications are a significant cause of morbidity in severe COVID-19 patients.
Intensive Care Unit (ICU) delirium is a key predictor of long-term cognitive decline in these patients.
Current management strategies require enhanced predictive tools for timely intervention.

Discussion:

Auto Machine Learning (ML) offers a state-of-the-art approach for predicting ICU delirium.
ML models can provide rapid, accurate risk-stratification for severe COVID-19 patients.
Integrating ML into clinical protocols can optimize neuropsychiatric care pathways.

Key Insights:

Developed ML models demonstrate high accuracy in predicting ICU delirium.
Predictive analytics enable proactive neurological management for COVID-19 survivors.
Early identification of delirium risk is crucial for mitigating cognitive impairment.

Outlook:

Further research into advanced ML algorithms can refine predictive capabilities.
Implementation of ML tools in critical care settings is recommended.
This approach holds potential to reduce morbidity and mortality associated with COVID-19 neuropsychiatric sequelae.