Predicting 30-days mortality for MIMIC-III patients with sepsis-3: a machine learning approach using...
Predicting 30-days mortality for MIMIC-III patients with sepsis-3: a machine learning approach using XGboost
About this item
Full title
Author / Creator
Hou, Nianzong , Li, Mingzhe , He, Lu , Xie, Bing , Wang, Lin , Zhang, Rumin , Yu, Yong , Sun, Xiaodong , Pan, Zhengsheng and Wang, Kai
Publisher
England: BioMed Central Ltd
Journal title
Language
English
Formats
Publication information
Publisher
England: BioMed Central Ltd
Subjects
More information
Scope and Contents
Contents
Sepsis is a significant cause of mortality in-hospital, especially in ICU patients. Early prediction of sepsis is essential, as prompt and appropriate treatment can improve survival outcomes. Machine learning methods are flexible prediction algorithms with potential advantages over conventional regression and scoring system. The aims of this study...
Alternative Titles
Full title
Predicting 30-days mortality for MIMIC-III patients with sepsis-3: a machine learning approach using XGboost
Authors, Artists and Contributors
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Primary Identifiers
Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_7f5fed9e15f34fccba407542deb52b8c
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_7f5fed9e15f34fccba407542deb52b8c
Other Identifiers
ISSN
1479-5876
E-ISSN
1479-5876
DOI
10.1186/s12967-020-02620-5
