Prediction of stroke-associated hospital-acquired pneumonia: Machine learning approach.

Journal: Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association

PMID: 39674434

Abstract

BACKGROUND: Stroke-associated Hospital Acquired Pneumonia (HAP) significantly impacts patient outcomes. This study explores the utility of machine learning models in predicting HAP in stroke patients, leveraging national registry data and SHapley Additive exPlanations (SHAP) analysis to identify key predictive factors.

Authors

Ahmad A Abujaber

Nursing Department, Hamad Medical Corporation, P.O. Box 3050, Doha, Qatar.
Said Yaseen

School of Medicine, Jordan University of Science and Technology, Irbid, Jordan.
Abdulqadir J Nashwan

Nursing Department, Hamad Medical Corporation, P.O. Box 3050, Doha, Qatar. anashwan@hamad.qa.
Naveed Akhtar

Neurology Section, Neuroscience Institute, Hamad Medical Corporation, Doha, Qatar.
Yahia Imam

Neurology Section, Neuroscience Institute, Hamad Medical Corporation, Doha, Qatar.

Keywords

Aged Aged, 80 and over Decision Support Techniques Female Healthcare-Associated Pneumonia Hemorrhagic Stroke Humans Ischemic Stroke Machine Learning Male Middle Aged Predictive Value of Tests Prognosis Registries Risk Assessment Risk Factors Stroke

External Resources

View on PubMed Access via DOI PubMed (39674434)

Prediction of stroke-associated hospital-acquired pneumonia: Machine learning approach.

Abstract

Authors

Keywords

External Resources

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