Deep learning model to identify and validate hypotension endotypes in surgical and critically ill patients.

ABSTRACT:

Background

Hypotension is associated with organ injury and death in surgical and critically ill patients. In clinical practice, treating hypotension remains challenging because it can be caused by various underlying haemodynamic alterations. We aimed to identify and independently validate endotypes of hypotension in big datasets of surgical and critically ill patients using unsupervised deep learning.

Methods

We developed an unsupervised deep learning algorithm, specifically a deep learning autoencoder model combined with a Gaussian mixture model, to identify endotypes of hypotension based on stroke volume index, heart rate, systemic vascular resistance index, and stroke volume variation observed during episodes of hypotension. The algorithm was developed with data from 871

SUBMITTER: Jian Z 

PROVIDER: S-EPMC11775843 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature