Refinement of machine learning arterial waveform models for predicting blood loss in canines.

ABSTRACT:

Introduction

Hemorrhage remains a leading cause of death in civilian and military trauma. Hemorrhages also extend to military working dogs, who can experience injuries similar to those of the humans they work alongside. Unfortunately, current physiological monitoring is often inadequate for early detection of hemorrhage. Here, we evaluate if features extracted from the arterial waveform can allow for early hemorrhage prediction and improved intervention in canines.

Methods

In this effort, we extracted more than 1,900 features from an arterial waveform in canine hemorrhage datasets prior to hemorrhage, during hemorrhage, and during a shock hold period. Different features were used as input to decision tree machine learning (ML) model architectures to track three model predict

SUBMITTER: Gonzalez JM 

PROVIDER: S-EPMC11371769 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature