Using a machine learning approach to predict mortality in critically ill influenza patients: a cross-sectional retrospective multicentre study in Taiwan.

ABSTRACT:

Objectives

Current mortality prediction models used in the intensive care unit (ICU) have a limited role for specific diseases such as influenza, and we aimed to establish an explainable machine learning (ML) model for predicting mortality in critically ill influenza patients using a real-world severe influenza data set.

Study design

A cross-sectional retrospective multicentre study in Taiwan SETTING: Eight medical centres in Taiwan.

Participants

A total of 336 patients requiring ICU-admission for virology-proven influenza at eight hospitals during an influenza epidemic between October 2015 and March 2016.

Primary and secondary outcome measures

We employed extreme gradient boosting (XGBoost) to establish the prediction model, compared the performance with logis

SUBMITTER: Hu CA 

PROVIDER: S-EPMC7045134 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature