Table 2.

Best predictive performance from each random forest patient similarity metric (PSM) model in terms of mean area under the receiver operating characteristic curve and area under the precision-recall curve in comparison with cosine PSM and traditional models with no PSM. All cosine PSM results are from Lee et al [14].

	Number of similar patients at best predictive performance				Best predictive performance, mean (95% CI)
	AUROCa		AUPRCb		AUROC			AUPRC
	RFc PSMd	Cosine PSM	RF PSM	Cosine PSM	RF PSM	Cosine PSM	No PSM	RF PSM	Cosine PSM	No PSM
DCe	260	100	230	60	0.801 (0.792-0.811)	0.797 (0.791-0.803)	0.693 (0.679-0.707)	0.429 (0.409-0.449)	0.393 (0.378-0.407)	0.280 (0.263-0.297)
LRf	5000	6000	9000	6000	0.824 (0.815-0.832)	0.830 (0.825-0.836)	0.811 (0.799-0.821)	0.460 (0.437-0.484)	0.474 (0.460-0.488)	0.449 (0.430-0.468)
DTg	5000	2000	7000	4000	0.779 (0.775-0.784)	0.753 (0.742-0.764)	0.721 (0.705-0.738)	0.352 (0.337-0.367)	0.347 (0.335-0.358)	0.339 (0.324-0.353)
RF	15000	—	4000	—	0.839 (0.835-0.844)	—	0.839 (0.835-0.844)	0.507 (0.527-0.486)	—	0.505 (0.487-0.523)
CSRFh	—	—	—	—	0.832 (0.821-0.843)	—	—	0.496 (0.520-0.471)	—	—

^aAUROC: area under the receiver operating characteristic curve.

^bAUPRC: area under the precision-recall curve.

^cRF: random forest.

^dPSM: patient similarity metric.

^eDC: death counting.

^fLR: logistic regression.

^gDT: decision tree.

^hCSRF: case-specific random forest.