Machine learning prediction model for postoperative outcome after perforated appendicitis.

ABSTRACT:

Purpose

Appendectomy for acute appendicitis is one of the most common operative procedures worldwide in both children and adults. In particular, complicated (perforated) cases show high variability in individual outcomes. Here, we developed and validated a machine learning prediction model for postoperative outcome of perforated appendicitis.

Methods

Retrospective analyses of patients with clinically and histologically verified perforated appendicitis over 10 years were performed. Demographic and surgical baseline characteristics were used as competing predictors of single-patient outcomes along multiple dimensions via a random forest classifier with stratified subsampling. To assess whether complications could be predicted in new, individual cases, the ensuing models were e

SUBMITTER: Eickhoff RM 

PROVIDER: S-EPMC8933368 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature