Predicting 30-days mortality for MIMIC-III patients with sepsis-3: a machine learning approach using XGboost.

ABSTRACT:

Background

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 were to develop a machine learning approach using XGboost to predict the 30-days mortality for MIMIC-III Patients with sepsis-3 and to determine whether such model performs better than traditional prediction models.

Methods

Using the MIMIC-III v1.4, we identified patients with sepsis-3. The data was split into two groups based on death or survival within 30 days and variables, selected based on clinical significance and

SUBMITTER: Hou N 

PROVIDER: S-EPMC7720497 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature