Refining electronic medical records representation in manifold subspace

BMC Bioinformatics

Table 3 Three basic models use different types of pre-trained word embeddings to predict performance

Method	Embedding	Macro AUC	Micro AUC	Macro F1	Micro F1	Test loss value	Top-10 recall
RNN	Random	0.854	0.972	0.204	0.653	0.032	0.772
	FastText	0.842	0.973	0.149	0.628	0.032	0.774
	Glove	0.861	0.974	0.219	0.656	0.031	0.788
	Word2Vec	0.851	0.974	0.165	0.642	0.031	0.783
	BERT	0.500	0.908	0.000	0.000	0.061	0.442
	ALBERT	0.503	0.915	0.026	0.018	0.054	0.446
	BioBERT	0.513	0.923	0.051	0.038	0.052	0.457
	BlueBERT	0.533	0.939	0.075	0.043	0.050	0.471
	Ours	0.857	0.976	0.182	0.659	0.030	0.793
CNN	Random	0.825	0.968	0.214	0.626	0.040	0.753
	FastText	0.665	0.921	0.012	0.223	0.053	0.488
	Glove	0.842	0.972	0.188	0.622	0.034	0.767
	Word2Vec	0.692	0.925	0.021	0.313	0.052	0.492
	BERT	0.549	0.906	0.000	0.000	0.059	0.442
	ALBERT	0.556	0.914	0.014	0.012	0.053	0.453
	BioBERT	0.559	0.921	0.015	0.041	0.047	0.459
	BlueBERT	0.567	0.929	0.021	0.047	0.042	0.464
	Ours	0.852	0.974	0.217	0.628	0.038	0.779
CAML	Random	0.855	0.978	0.257	0.656	0.032	0.806
	FastText	0.856	0.980	0.270	0.656	0.031	0.809
	Glove	0.867	0.978	0.272	0.647	0.033	0.801
	Word2Vec	0.855	0.980	0.274	0.662	0.030	0.813
	BERT	0.497	0.908	0.000	0.000	0.058	0.442
	ALBERT	0.505	0.916	0.026	0.022	0.054	0.457
	BioBERT	0.513	0.924	0.045	0.041	0.048	0.465
	BlueBERT	0.534	0.934	0.060	0.076	0.042	0.478
	Ours	0.886	0.982	0.270	0.673	0.029	0.823

ISSN: 1471-2105