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Developing an Interpretable Machine Learning Model to Predict in-Hospital Mortality in Sepsis Patients: A Retrospective Temporal Validation Study.
Li, Shuhe; Dou, Ruoxu; Song, Xiaodong; Lui, Ka Yin; Xu, Jinghong; Guo, Zilu; Hu, Xiaoguang; Guan, Xiangdong; Cai, Changjie.
Affiliation
  • Li S; Department of Critical Care, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou 510080, China.
  • Dou R; Department of Critical Care, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou 510080, China.
  • Song X; Department of Critical Care, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou 510080, China.
  • Lui KY; Department of Critical Care, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou 510080, China.
  • Xu J; Department of Anesthesiology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou 510080, China.
  • Guo Z; Department of Statistics, William March Rice University, 6100 Main St, Houston, TX 77005, USA.
  • Hu X; Department of Critical Care, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou 510080, China.
  • Guan X; Department of Critical Care, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou 510080, China.
  • Cai C; Department of Critical Care, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou 510080, China.
J Clin Med ; 12(3)2023 Jan 24.
Article in En | MEDLINE | ID: mdl-36769564
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Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prognostic_studies / Risk_factors_studies Language: En Journal: J Clin Med Year: 2023 Type: Article Affiliation country: China
Fulltext
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PubMed Links
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Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prognostic_studies / Risk_factors_studies Language: En Journal: J Clin Med Year: 2023 Type: Article Affiliation country: China