Development and validation of a clinical prediction model for in-hospital mortality of severe pneumonia based on machine learning.

ABSTRACT:

Objective

We aimed to develop an interpretable model to predict the mortality risk for severe pneumonia patients.

Methods

The study retrospectively employed data from severe pneumonia patients at two hospitals as the training set for the model development. Patients with severe pneumonia admitted from the same two hospitals were prospectively included as the test set for the model evaluation. A total of 115 candidate features were extracted based on clinical relevance and existing literature. The least absolute shrinkage and selection operator (LASSO) regression was applied to select features for the establishment of five models: logistic regression (LR), support vector machine (SVM), decision tree (DT), random forest (RF) and extreme gradient boosting (XGBoost). The performa

SUBMITTER: Xie K 

PROVIDER: S-EPMC12689516 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature