Early prediction of intraoperative hypothermia in patients undergoing gynecological laparoscopic surgery: A retrospective cohort study.

ABSTRACT: Intraoperative hypothermia is one of the most common adverse events related to surgery, and clinical practice has been severely underestimated. In view of this, this study aims to build a practical intraoperative hypothermia prediction model for clinical decision-making assistance. We retrospectively collected clinical data of patients who underwent gynecological laparoscopic surgery from June 2018 to May 2023, and constructed a multimodal algorithm prediction model based on this data. For the construction of the prediction model, all data are randomly divided into a training queue (70%) and a testing queue (30%), and then 3 types of machine learning algorithms are used, namely: random forest, artificial neural network, and generalized linear regression. The effectiveness evaluation of all

SUBMITTER: Lu Z 

PROVIDER: S-EPMC11460914 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature