Machine learning risk prediction model for bloodstream infections related to totally implantable venous access ports in patients with cancer.

ABSTRACT:

Objective

This study aimed to develop and validate a machine learning-based risk prediction model for catheter-related bloodstream infection (CRBSI) following implantation of totally implantable venous access ports (TIVAPs) in patients.

Methods

A retrospective cohort study design was employed, utilizing the R software package mlr3. Various algorithms including logistic regression, naive Bayes, K nearest neighbor, classification tree, and random forest were applied. Addressing class imbalance, benchmarks were used, and model performance was assessed using the area under the curve (AUC). The final model, chosen for its superior performance, was interpreted using variable importance scores. Additionally, a nomogram was developed to calculate individualized risk probabilities, e

SUBMITTER: Wang F 

PROVIDER: S-EPMC11324827 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature