Back to search

Article

Can machine learning or deep learning discover novel signatures of illness in continuous cardiorespiratory monitoring data?

2024-02-04

Abstract excerpt

<h4>Background</h4> Cardiorespiratory deterioration due to sepsis is a leading cause of morbidity and mortality for extremely premature infants with very low birth weight (VLBW, birthweight <1500g). Abnormal heart rate (HR) patterns precede the clinical diagnosis of late-onset sepsis in this population. Decades ago, clinicians recognized a pattern of reduced HR variability and increased HR decelerations in electro...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work
b6df0d12-db19-5229-9f6e-f9c4e7cbe5e8
DOI
10.1101/2024.02.03.24302230
Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest
Interactive article relationship graphSelect a related publication card to move it into the centre and load its closest explainable connections. Solid lines are source-backed structured connections. Dashed lines are semantic discovery signals and are not scientific evidence.
Can machine learning or deep learning discover novel signatures of illness in continuous cardiorespiratory monitoring data?DOI 10.1101/2024.02.03.24302230
Select a neighboring publication to make it the new centre.