What is the clinical evidence from this study?

Study design: Other. Population: Surgery cancellation. Intervention: Machine learning models (random forest, support vector machine, XGBoost). Primary outcome: Identification of surgeries with high risks of cancellation (AUC 0.682).

What does this research mean for the field?

Machine learning models, particularly random forest, can feasibly identify surgeries with high risks of cancellation, achieving an area under the receiver operating characteristic curve of 0.682. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

December 5, 2018Open Access

Machine learning for identification of surgeries with high risks of cancellation

Key Result

Machine learning models, particularly random forest, identified surgeries with high risks of cancellation with an area under the receiver operating characteristic curve of 0.682.

Structured PICO

Can machine learning models effectively identify surgeries with high risks of cancellation?

Population

Surgeries (no specific patient details or sample size provided in the abstract)

Intervention

Machine learning models (random forest, support vector machine, and XGBoost)

Outcome

Identification of surgeries with high risks of cancellation (measured by sensitivity, specificity, PPV, NPV, accuracy, and AUC)surrogate

Machine learning models, particularly random forest, can effectively identify surgeries with high risks of cancellation, facilitating better surgery resource management.

Main Result

Effect estimate: AUC 0.682

Abstract

Surgery cancellations waste scarce operative resources and hinder patients’ access to operative services. In this study, the Wilcoxon and chi-square tests were used for predictor selection, and three machine learning models – random forest, support vector machine, and XGBoost – were used for the identification of surgeries with high risks of cancellation. The optimal performances of the identification models were as follows: sensitivity − 0.615; specificity − 0.957; positive predictive value − 0.454; negative predictive value − 0.904; accuracy − 0.647; and area under the receiver operating characteristic curve − 0.682. Of the three models, the random forest model achieved the best performance. Thus, the effective identification of surgeries with high risks of cancellation is feasible with stable performance. Models and sampling methods significantly affect the performance of identification. This study is a new application of machine learning for the identification of surgeries with high risks of cancellation and facilitation of surgery resource management.

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