A Machine Learning Approach for Predicting Early Phase Postoperative Hypertension in Patients Undergoing Carotid Endarterectomy.

Journal: Annals of vascular surgery

PMID: 32653616

Abstract

BACKGROUND: This study aimed to establish and validate a machine learning-based model for the prediction of early phase postoperative hypertension (EPOH) requiring the administration of intravenous vasodilators after carotid endarterectomy (CEA).

Authors

Jinyun Tan

Department of Vascular Surgery, Huashan Hospital, Fudan University, Shanghai, China.
Qi Wang

Biotherapeutics Discovery Research Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China.
Weihao Shi

Department of Vascular Surgery, Huashan Hospital, Fudan University, Shanghai, China.
Kun Liang

Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, N2L 3G1, Canada. kun.liang@uwaterloo.ca.
Bo Yu

Department of Cardiology, The 2nd Affiliated Hospital of Harbin Medical University, Harbin, China.
Qingqing Mao

Dascena Inc., Hayward, California, USA.

Keywords

Administration, Intravenous Adult Aged Aged, 80 and over Antihypertensive Agents Blood Pressure Carotid Stenosis Cerebrovascular Circulation Cerebrovascular Disorders Databases, Factual Decision Support Techniques Endarterectomy, Carotid Female Humans Hypertension Machine Learning Male Middle Aged Predictive Value of Tests Retrospective Studies Risk Assessment Risk Factors Time Factors Treatment Outcome Vasodilator Agents

External Resources

View on PubMed Access via DOI PubMed (32653616)

A Machine Learning Approach for Predicting Early Phase Postoperative Hypertension in Patients Undergoing Carotid Endarterectomy.

Abstract

Authors

Keywords

External Resources

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