Application of machine learning to the prediction of postoperative sepsis after appendectomy.

ABSTRACT:

Background

We applied various machine learning algorithms to a large national dataset to model the risk of postoperative sepsis after appendectomy to evaluate utility of such methods and identify factors associated with postoperative sepsis in these patients.

Methods

The National Surgery Quality Improvement Program database was used to identify patients undergoing appendectomy between 2005 and 2017. Logistic regression, support vector machines, random forest decision trees, and extreme gradient boosting machines were used to model the occurrence of postoperative sepsis.

Results

In the study, 223,214 appendectomies were identified; 2,143 (0.96%) were indicated as having postoperative sepsis. Logistic regression (area under the curve 0.70; 95% confidence interval, 0.68-

SUBMITTER: Bunn C 

PROVIDER: S-EPMC7927311 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature