Machine learning predictive model for aspiration risk in early enteral nutrition patients with severe acute pancreatitis.

ABSTRACT:

Background

The aim of this study was to build and validate a risk prediction model for aspiration in severe acute pancreatitis patients receiving early enteral nutrition (EN) by identifying risk factors for aspiration in these patients.

Methods

The risk factors for aspiration were analyzed to build a prediction model based on the data collected from 339 patients receiving enteral nutrition. Subsequently, we used six machine learning algorithms and the model was validated by the area under the curve.

Results

In this study, the collected data were divided into two groups: a training cohort and a validation cohort. The results showed that 28.31 % (77) of patients had aspiration and 71.69 % (195) of patients had non-aspiration in training cohort. Moreover, age, consciousn

SUBMITTER: Zhang B 

PROVIDER: S-EPMC11626782 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature