Table 3 Comparison between STL and MTL in stroke prediction. MAND-LR, MAND-MLP, MAND-LSTM, and MAND-MHSA denote the MAND architecture integrated with logistic regression, multilayer perceptron (MLP), LSTM, and multi-head self-attention as ICD feature extraction modules, respectively. FM and DCN represent CTR-based approaches. BAC: balanced accuracy; FPR: false positive rate; FNR: false negative rate.

Backbone model	STL/MTL	Log loss	AUC	BAC	Precision	Recall	F1 score	FPR	FNR
MAND-LR¹⁸	STL	0.2120	0.8585	0.6023	0.7794	0.2102	0.3311	0.0056	0.7898
MAND-LR¹⁸	MTL	0.2134	0.8586	0.6009	0.7857	0.2071	0.3279	0.0053	0.7929
MAND-MLP¹⁸	STL	0.2042	0.8626	0.6169	0.8939	0.2364	0.3739	0.0026	0.7636
MAND-MLP¹⁸	MTL	0.2198	0.8467	0.5694	0.8637	0.1409	0.2422	0.0021	0.8591
MAND-LSTM¹⁸	STL	0.1974	0.8700	0.6460	0.8339	0.2977	0.4387	0.0057	0.7023
MAND-LSTM¹⁸	MTL	0.2050	0.8625	0.6286	0.8240	0.2626	0.3983	0.0054	0.7374
MAND-MHSA¹⁸	STL	0.2020	0.8703	0.6445	0.7540	0.2982	0.4274	0.0092	0.7018
MAND-MHSA¹⁸	MTL	0.2076	0.8643	0.6392	0.7367	0.2882	0.4143	0.0098	0.7118
FM¹⁹	STL	0.2619	0.8330	0.6226	0.5708	0.2642	0.3612	0.0190	0.7358
FM¹⁹	MTL	0.2288	0.8351	0.6216	0.6718	0.2551	0.3698	0.0119	0.7449
DCN²²	STL	0.2013	0.8649	0.6336	0.8600	0.2715	0.4127	0.0043	0.7285
DCN²²	MTL	0.2040	0.8627	0.6288	0.8509	0.2620	0.4003	0.0044	0.7380

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