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Review
. 2024 May 11;31(5):2727-2747.
doi: 10.3390/curroncol31050207.

Application of Machine Learning in Predicting Perioperative Outcomes in Patients with Cancer: A Narrative Review for Clinicians

Garry Brydges  1 , Abhineet Uppal  2 , Vijaya Gottumukkala  3
Affiliations
Review

Application of Machine Learning in Predicting Perioperative Outcomes in Patients with Cancer: A Narrative Review for Clinicians

Garry Brydges et al. Curr Oncol. .

Abstract

This narrative review explores the utilization of machine learning (ML) and artificial intelligence (AI) models to enhance perioperative cancer care. ML and AI models offer significant potential to improve perioperative cancer care by predicting outcomes and supporting clinical decision-making. Tailored for perioperative professionals including anesthesiologists, surgeons, critical care physicians, nurse anesthetists, and perioperative nurses, this review provides a comprehensive framework for the integration of ML and AI models to enhance patient care delivery throughout the perioperative continuum.

Keywords: artificial intelligence; machine learning; neural networks; perioperative outcomes.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Perioperative Cancer Outcomes Group (PCOG) conceptual framework [8,21]. RedCap: Research Electronic Data Capture; NSQIP: National Surgical Quality Improvement Program; MPOG: Multicenter Perioperative Outcomes Group; WEBi: BusinessObjects Web Intelligence; SEER: Surveillance, Epidemiology, and End Results Program; PACS: Picture Archiving and Communication System; API: application programming interface; POI: postoperative ileus; AKI: acute kidney injury; RIOT: Return to Intended Oncologic Treatment.
Figure 2
Figure 2
Linear regression versus logistic regression comparison.
Figure 3
Figure 3
Confusion matrix [8,21].
Figure 4
Figure 4
Area under the ROC curve [8,21,22].
Figure 5
Figure 5
Ensemble techniques in machine learning [16,18,31].
Figure 6
Figure 6
Bootstrapping [8,21]. Each color represents a decision tree in the initial dataset. A collection of decision trees allows for a more stable technique to be used called Random Forest. Random forest randomly selects a set of decision trees through bootstrap aggregation known as bagging.
See this image and copyright information in PMC

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