Application of Machine Learning Models to Biomedical and Information System Signals From Critically Ill Adults.

ABSTRACT:

Background

Machine learning (ML)-derived notifications for impending episodes of hemodynamic instability and respiratory failure events are interesting because they can alert physicians in time to intervene before these complications occur.

Research question

Do ML alerts, telemedicine system (TS)-generated alerts, or biomedical monitors (BMs) have superior performance for predicting episodes of intubation or administration of vasopressors?

Study design and methods

An ML algorithm was trained to predict intubation and vasopressor initiation events among critically ill adults. Its performance was compared with BM alarms and TS alerts.

Results

ML notifications were substantially more accurate and precise, with 50-fold lower alarm burden than TS alerts for predicti

SUBMITTER: Lilly CM 

PROVIDER: S-EPMC11214904 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature