Predicting outcomes in older ED patients with influenza in real time using a big data-driven and machine learning approach to the hospital information system.

ABSTRACT:

Background

Predicting outcomes in older patients with influenza in the emergency department (ED) by machine learning (ML) has never been implemented. Therefore, we conducted this study to clarify the clinical utility of implementing ML.

Methods

We recruited 5508 older ED patients (≥65 years old) in three hospitals between 2009 and 2018. Patients were randomized into a 70%/30% split for model training and testing. Using 10 clinical variables from their electronic health records, a prediction model using the synthetic minority oversampling technique preprocessing algorithm was constructed to predict five outcomes.

Results

The best areas under the curves of predicting outcomes were: random forest model for hospitalization (0.840), pneumonia (0.765), and sepsis or septic

SUBMITTER: Tan TH 

PROVIDER: S-EPMC8077903 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature