. 2020 Apr 29;8(4):e17787. doi: 10.2196/17787

Table 2.

ROUGE^a F1 results of diagnoses with incorrect words.

ROUGE-L^b	BERT^c	BioBERT^d	LSTM^e	Proposed Model
Diagnoses without error words (n=451)^f	0.704	0.717	0.651	0.698
Diagnoses with incorrect words (n=138)	0.676	0.692	0.640	0.674

^aROUGE: Recall-Oriented Understudy for Gisting Evaluation.

^bROUGE-L: ROUGE for the longest common subsequence.

^cBERT: Bidirectional Encoder Representations from Transformers.

^dBioBERT: Bidirectional Encoder Representations from Transformers trained on a biomedical corpus.

^eLSTM: Long Short-Term Memory.

^fn represents the number of reference labels.