Learning Conceptual-Contextual Embeddings for Medical Text
Table 4: Readmission prediction performance
['[BOLD] Model', '[BOLD] Acc', '[BOLD] Pre-0', '[BOLD] Pre-1', '[BOLD] Re-0', '[BOLD] Re-1', '[BOLD] A.R.', '[BOLD] A.P.']
Lin et al. uses chart events, demographic information and diagnosis as input to a LSTM+CNN model. We only use written note text and none of the numerical and time-series information. The primary metric area under ROC clearly shows that the CC model produces a performance boost over the baseline and surpassed state-of-the-art results.