Automated detection of altered mental status in emergency department clinical notes: a deep learning approach

Table 4 Accuracy and area under the ROC curve (AUC) results for bag of words (BOW)-based models and the word embedding-based deep learning models along with 95% confidence intervals (CI)

Category	Model^a	AUC (95% CI)	Accuracy	Epochs
BOW models	RF	0.975 (0.967–0.983)	0.921	N/A
	LASS	0.973 (0.964–0.982)	0.912	N/A
	SVM	0.967 (0.957–0.976)	0.912	N/A
	MLP	0.947 (0.934–0.960)	0.883	N/A
	SDT	0.934 (0.918–0.950)	0.911	N/A
	NBC	0.924 (0.908–0.940)	0.838	N/A
Deep learning models	CNN_D200	0.985 (0.979–0.992)	0.945	30.8
	CNN_W2V	0.985 (0.979–0.991)	0.942	25.0
	CNN_D50	0.984 (0.978–0.991)	0.944	36.6

^aModel abbreviations are described in the text
The number of epochs for training the deep learning is based on the early stopping condition as described in the methods. The entries are sorted in descending order of AUC within each category. Bolding indicates results for the best performing models