Machine learning methods for perioperative anesthetic management in cardiac surgery patients: a scoping review.

ABSTRACT:

Background

Machine learning (ML) is developing fast with promising prospects within medicine and already has several applications in perioperative care. We conducted a scoping review to examine the extent and potential limitations of ML implementation in perioperative anesthetic care, specifically in cardiac surgery patients.

Methods

We mapped the current literature by searching three databases: MEDLINE (Ovid), EMBASE (Ovid), and Cochrane Library. Articles were eligible if they reported on perioperative ML use in the field of cardiac surgery with relevance to anesthetic practices. Data on the applicability of ML and comparability to conventional statistical methods were extracted.

Results

Forty-six articles on ML relevant to the work of the anesthesiologist in cardiac

SUBMITTER: Rellum SR 

PROVIDER: S-EPMC8743411 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature