Patient representation learning and interpretable evaluation using clinical notes
Table 1: Classification results on different tasks using the BoW features, the SDAE representations computed from the BoW (SDAE-BoW), the doc2vec representations, the concatenated SDAE-BoW and doc2vec representations ([doc2vec, SDAE-BoW]) with Cohen’s κ score, the BoCUI features, and the SDAE vectors computed from the BoCUI (SDAE-BoCUI). AUC-ROC values are reported for the mortality tasks, and weighted F-score for the others.
['[BOLD] No.', '[BOLD] Approach', '[BOLD] Mortality  [BOLD] In_hosp', '[BOLD] Mortality  [BOLD] 30_days', '[BOLD] Mortality  [BOLD] 1_year', '[BOLD] Pri_diag_cat', '[BOLD] Pri_proc_cat', '[BOLD] Gender']
the SDAE-BoCUI (the initial SDAE input is BoCUI), and the doc2vec models as input features for different tasks, as opposed to using the BoW and the BoCUI sparse features. We find that the agreement scores are not high, which may indicate that the models learn complimentary information. We then concatenate the two dense representations (model ensemble) to analyze model complementarity.