Table 2.

Performance comparison of different embeddings on the downstream risk prediction task. For each evaluation metric, the best performance is highlighted in bold, and the second-best result is underlined.

Embedding	Method	MIMIC-III				PROMOTE
		Accuracy(%)	AUROC(%)	AUCPR(%)	Macro-F1(%)	Accuracy(%)	AUROC(%)	AUCPR(%)	Macro-F1(%)
Binary Embedding	LR	78.58±0.18	81.23±0.14	68.22±0.32	45.79±0.44	74.08±0.69	60.56±1.96	30.67±1.91	53.84±1.37
	SVM	80.41±0.10	83.21±0.23	71.29±0.37	41.77±0.04	77.42±0.69	63.13±1.20	33.48±1.61	44.28±0.26
	MLP	76.05±0.11	78.09±0.22	63.74±0.25	44.21±0.22	69.30±0.58	57.04±1.13	28.22±1.92	53.46±0.71
	RF	77.17±0.25	81.22±0.11	67.45±0.40	23.60±0.21	77.40±0.11	64.73±1.33	32.77±1.55	43.80±1.12
	XGB	79.62±0.20	82.72±0.15	70.83±0.21	49.06±0.34	77.11±0.91	65.01±1.70	33.30±2.42	49.84±1.17
KG Embedding	LR	77.72±0.22	80.66±0.09	66.20±0.36	35.80±0.34	77.40±0.82	61.62±1.70	29.79±2.41	44.08±0.34
	SVM	78.38±0.22	80.84±0.19	67.49±0.39	35.40±0.30	77.49±0.83	54.97±1.39	24.49±0.94	43.78±0.26
	MLP	72.08±0.21	73.12±0.21	53.79±0.54	42.47±0.39	68.77±1.11	57.64±1.55	24.05±6.48	53.39±1.81
	RF	76.57±0.17	78.98±0.16	64.36±0.56	25.13±0.95	77.59±0.83	56.49±1.69	25.79±1.53	43.75±0.26
	XGB	75.35±0.13	77.27±0.19	61.77±0.51	38.65±0.49	77.48±0.87	59.04±1.31	28.95±1.63	46.21±0.84
HypKG Embedding	LR	80.32±0.29	83.98±0.45	72.84±0.91	46.28±0.54	77.62±0.74	66.97±0.51	35.65±1.56	47.58±0.95
	SVM	80.51±0.16	83.07±0.35	71.59±0.62	42.83±0.24	77.70±0.90	58.07±1.22	28.49±1.93	44.06±0.54
	MLP	74.61±0.18	77.10±0.30	62.15±4.47	46.91±0.62	69.17±1.32	58.72±1.59	32.19±1.61	54.60±1.52
	RF	80.54±0.23	84.01±0.22	72.80±0.47	41.72±0.29	74.26±7.92	63.74±1.15	32.19±1.92	46.98±4.74
	XGB	78.86±0.21	81.89±0.61	69.25±0.63	47.12±0.63	77.02±0.52	64.22±1.28	32.65±2.85	49.43±0.82
	Hypergraph	80.86±0.17	84.26±0.17	73.12±0.37	45.30±0.49	77.06±0.62	67.37±1.02	35.74±1.67	53.28±1.54