Predicting early postoperative PONV using multiple machine-learning- and deep-learning-algorithms.

ABSTRACT:

Objective

PONV reduces patient satisfaction and increases hospital costs as patients remain in the hospital for longer durations. In this study, we build a preliminary artificial intelligence algorithm model to predict early PONV in patients.

Methods

We use R for statistical analysis and Python for the machine learning prediction model.

Results

Average characteristic engineering results showed that haloperidol, sex, age, history of smoking, and history of PONV were the first 5 contributing factors in the occurrence of early PONV. Test group results for artificial intelligence prediction of early PONV: in terms of accuracy, the four best algorithms were CNNRNN (0.872), Decision Tree (0.868), SVC (0.866) and adab (0.865); in terms of precision, the three best algorithms

SUBMITTER: Zhou CM 

PROVIDER: S-EPMC10230679 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature