Development, deployment, and continuous monitoring of a machine learning model to predict respiratory failure in critically ill patients.

ABSTRACT:

Objectives

This study describes the development and deployment of a machine learning (ML) model called Vent.io to predict mechanical ventilation (MV).

Materials and methods

We trained Vent.io using electronic health record data of adult patients admitted to the intensive care units (ICUs) of the University of California San Diego (UCSD) Health System. We prospectively deployed Vent.io using a real-time platform at UCSD and evaluated the performance of Vent.io for a 1-month period in silent mode and on the MIMIC-IV dataset. As part of deployment, we included a Predetermined Changed Control Plan (PCCP) for continuous model monitoring that triggers model fine-tuning if performance drops below a specified area under the receiver operating curve (AUC) threshold of 0.85.

Result

SUBMITTER: Lam JY 

PROVIDER: S-EPMC11633942 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature