Table 2.

Performance comparison of machine learning models trained with different data sources.

		Metricsb
Parametera	Model	AUROCc curve	Sensitivity	Specificity	Precision	F1 score
ACS-NSQIP SRCd		0.6333	0.9000	0.0370	0.4091	0.5625
Patient clinical characteristics	LRe	0.7054	0.9000	0.2321	0.4558	0.6051
Patient activity	SVMf	0.7027	0.9000	0.2107	0.4491	0.5992
Patient clinical characteristics + patient activity	GBTg	0.7875	0.9000	0.3929	0.5143	0.6545

^aParameters used for the models are summarized in Multimedia Appendix 1.

^bThe metrics for the machine learning models represent the average across all leave-one-subject-out cross-validation folds.

^cAUROC: area under the receiver operating characteristic.

^dAmerican College of Surgeons National Surgical Quality Improvement Program surgical risk calculator (ACS-NSQIP SRC) was used as the baseline model for complications from pancreatoduodenectomy.

^eLR: logistic regression.

^fSVM: support vector machine.

^gGBT: gradient boosted trees.