Pediatric cardiac surgery: machine learning models for postoperative complication prediction.

Journal: Journal of anesthesia

PMID: 39028323

Abstract

PURPOSE: Managing children undergoing cardiac surgery with cardiopulmonary bypass (CPB) presents a significant challenge for anesthesiologists. Machine Learning (ML)-assisted tools have the potential to enhance the recognition of patients at risk of complications and predict potential issues, ultimately improving outcomes.

Authors

Rémi Florquin

Department of Anesthesiology, CHU Charleroi, Chaussée de Bruxelles 140, 6042, Lodelinsart, Belgium. remi.florquin@gmail.com.
Renaud Florquin

Floconsult SPRL, 1480, Tubize, Belgium.
Denis Schmartz

Department of Anesthesiology, Hôpital Universitaire de Bruxelles (H.U.B), Université Libre de Bruxelles, 1070, Brussels, Belgium.
Philippe Dony

Department of Anesthesiology, CHU Charleroi, Chaussée de Bruxelles 140, 6042, Lodelinsart, Belgium.
Giovanni Briganti

University of Mons, Mons, Belgium.

Keywords

Area Under Curve Cardiac Surgical Procedures Cardiopulmonary Bypass Child Child, Preschool Female Humans Infant Logistic Models Machine Learning Male Postoperative Complications Risk Assessment Support Vector Machine

External Resources

View on PubMed Access via DOI PubMed (39028323)

Pediatric cardiac surgery: machine learning models for postoperative complication prediction.

Abstract

Authors

Keywords

External Resources

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