Development and Validation of an Explainable Machine Learning Model for Major Complications After Cytoreductive Surgery.

ABSTRACT:

Importance

Cytoreductive surgery (CRS) is one of the most complex operations in surgical oncology with significant morbidity, and improved risk prediction tools are critically needed. Machine learning models can potentially overcome the limitations of traditional multiple logistic regression (MLR) models and provide accurate risk estimates.

Objective

To develop and validate an explainable machine learning model for predicting major postoperative complications in patients undergoing CRS.

Design, setting, and participants

This prognostic study used patient data from tertiary care hospitals with expertise in CRS included in the US Hyperthermic Intraperitoneal Chemotherapy Collaborative Database between 1998 and 2018. Information from 147 variables was extracted to predic

SUBMITTER: Deng H 

PROVIDER: S-EPMC9133947 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature