Development and Validation of Machine Learning-Based Model for Hospital Length of Stay in Patients Undergoing Endovascular Interventional Embolization for Intracranial Aneurysms.

Journal: World neurosurgery

PMID: 39755150

Abstract

OBJECTIVE: This study was to explore the factors associated with prolonged hospital length of stay (LOS) in patients with intracranial aneurysms (IAs) undergoing endovascular interventional embolization and construct prediction model machine learning algorithms.

Authors

Jian Zhao

Key Laboratory of Intelligent Rehabilitation and Barrier-Free for the Disabled (Changchun University), Ministry of Education, Changchun University, Changchun 130012, China.
Yi Luo

Electrical and Computer Engineering Department, Bioengineering Department, University of California, Los Angeles, CA 90095 USA, and also with the California NanoSystems Institute, University of California, Los Angeles, CA 90095 USA.

Keywords

Adult Aged Aneurysm, Ruptured Embolization, Therapeutic Endovascular Procedures Female Humans Intracranial Aneurysm Length of Stay Machine Learning Male Middle Aged Retrospective Studies

External Resources

View on PubMed Access via DOI PubMed (39755150)

Development and Validation of Machine Learning-Based Model for Hospital Length of Stay in Patients Undergoing Endovascular Interventional Embolization for Intracranial Aneurysms.

Abstract

Authors

Keywords

External Resources

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