Research on a machine learning-based predictive model for postoperative neurological dysfunction in acute Stanford type A aortic dissection

The XGBoost algorithm demonstrates superior efficacy in predicting postoperative ND in acute ATAAD patients, providing postoperative early warning, identifying high-risk patients, offering clinical guidance, and enabling timely intervention.

Authors

Xiaoyong Jing
Lei Qin (ORCID: https://orcid.org/0000-0001-9177-0753)
Li Lu (ORCID: https://orcid.org/0009-0005-5508-2503)
J. Zhu
Ruiyi Wang

Institutions

University of International Business and Economics (CN)
Capital Medical University (CN)
Institute of Economic Development (GB)
Beijing Anzhen Hospital (CN)
Institute of Economic and Social Development (AR)

Publication Details

Journal: Frontiers in Medicine
Published: 2026-02-13
DOI: https://doi.org/10.3389/fmed.2026.1716649
Primary Topic: Aortic Disease and Treatment Approaches
Type: article
Field-Weighted Citation Impact: 0.00

Funders

National Science and Technology Major Project

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article

Research on a machine learning-based predictive model for postoperative neurological dysfunction in acute Stanford type A aortic dissection

Xiaoyong Jing, Lei Qin, Li Lu, J. Zhu et al.

Feb 13, 2026

Frontiers in Medicine

Aortic Disease and Treatment Approaches

article

Research on a machine learning-based predictive model for postoperative neurological dysfunction in acute Stanford type A aortic dissection

Xiaoyong Jing, Lei Qin, Li Lu, J. Zhu, Ruiyi Wang

February 13, 2026article en

Abstract

The XGBoost algorithm demonstrates superior efficacy in predicting postoperative ND in acute ATAAD patients, providing postoperative early warning, identifying high-risk patients, offering clinical guidance, and enabling timely intervention.

Frontiers in MedicineVol. 13

University of International Business and Economics (CN), Capital Medical University (CN), Institute of Economic Development (GB), Beijing Anzhen Hospital (CN), Institute of Economic and Social Development (AR)

National Science and Technology Major Project

Openalex Percentile: Top 58%

Aortic Disease and Treatment Approaches

DOI Open Access PDF OpenAlex

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