Machine Learning Models for Predicting In-Hospital Mortality in Acute Aortic Dissection Patients.

Guo, Tuo; Fang, Zhuo; Yang, Guifang; Zhou, Yang; Ding, Ning; Peng, Wen; Gong, Xun; He, Huaping; Pan, Xiaogao; Chai, Xiangping

Guo, Tuo; Fang, Zhuo; Yang, Guifang; Zhou, Yang; Ding, Ning; Peng, Wen; Gong, Xun; He, Huaping; Pan, Xiaogao; Chai, Xiangping.

Afiliación

Guo T; Department of Emergency Medicine, The Second Xiangya Hospital, Central South University, Changsha, China.
Fang Z; Emergency Medicine and Difficult Diseases Institute, Central South University, Changsha, China.
Yang G; Trauma Center, Changsha, China.
Zhou Y; College of Information Science and Engineering, Hunan Normal University, Changsha, China.
Ding N; Department of Emergency Medicine, The Second Xiangya Hospital, Central South University, Changsha, China.
Peng W; Emergency Medicine and Difficult Diseases Institute, Central South University, Changsha, China.
Gong X; Trauma Center, Changsha, China.
He H; Department of Emergency Medicine, The Second Xiangya Hospital, Central South University, Changsha, China.
Pan X; Emergency Medicine and Difficult Diseases Institute, Central South University, Changsha, China.
Chai X; Trauma Center, Changsha, China.

Front Cardiovasc Med ; 8: 727773, 2021.

Article en En | MEDLINE | ID: mdl-34604356

Palabras clave

acute aortic dissection; extreme gradient boost; in-hospital mortality; machine learning; prediction

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Front Cardiovasc Med Año: 2021 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza

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