Outcomes	AUC	Precision	Recall
30-day Readmission Using Deep Learning Models
Using Small Embedding (Age ≥ 18)	0.597	0.225	0.298
Using Big Embedding (Age ≥ 18)	0.568	0.270	0.188
Using Small Embedding (18 ≤ Age < 65)	0.557	0.250	0.313
Using Big Embedding (18 ≤ Age < 65)	0.488	0.154	0.145
30-day Readmission Using Traditional Machine Learning Models (Age ≥ 18)
Logistic Regression	0.510	0.476	0.020
Random Forest	0.506	0.511	0.012
XGBoost	0.507	0.677	0.015
90-day Readmission Using Deep Learning Models
Using Small Embedding (Age ≥ 18)	0.614	0.35	0.409
Using Big Embedding (Age ≥ 18)	0.581	0.331	0.329
Using Small Embedding (18 ≤ Age < 65)	0.861	0.652	0.928
Using Big Embedding (18 ≤ Age < 65)	0.837	0.634	0.885
90-day Readmission Using Traditional Machine Learning Models (Age ≥ 18)
Logistic Regression	0.509	0.494	0.020
Random Forest	0.508	0.521	0.016
XGBoost	0.508	0.577	0.016