Comparing supervised machine learning algorithms for the prediction of partial arterial pressure of oxygen during craniotomy.

ABSTRACT:

Background and objectives

Brain tissue oxygenation is usually inferred from arterial partial pressure of oxygen (paO₂), which is in turn often inferred from pulse oximetry measurements or other non-invasive proxies. Our aim was to evaluate the feasibility of continuous paO₂ prediction in an intraoperative setting among neurosurgical patients undergoing craniotomies with modern machine learning methods.

Methods

Data from routine clinical care of lung-healthy neurosurgical patients were extracted from databases of the respective clinical systems and normalized. We used recursive feature elimination to identify relevant features for the prediction of paO₂. Six machine learning regression algorithms (gradient boosting, k-nearest neighbors, random

SUBMITTER: Gutmann AS 

PROVIDER: S-EPMC12406590 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature