Compact machine learning model for the accurate prediction of first 24-hour survival of mechanically ventilated patients.

ABSTRACT:

Background

The field of machine learning has been evolving and applied in medical applications. We utilised a public dataset, MIMIC-III, to develop compact models that can accurately predict the outcome of mechanically ventilated patients in the first 24 h of first-time hospital admission.

Methods

67 predictive features, grouped into 6 categories, were selected for the classification and prediction task. 4 tree-based algorithms (Decision Tree, Bagging, eXtreme Gradient Boosting and Random Forest), and 5 non-tree-based algorithms (Logistic Regression, K-Nearest Neighbour, Linear Discriminant Analysis, Support Vector Machine and Naïve Bayes), were employed to predict the outcome of 18,883 mechanically ventilated patients. 5 scenarios were crafted to mirror the target populatio

SUBMITTER: Nguyen QT 

PROVIDER: S-EPMC11222318 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature