 This study aimed to develop a deep learning model to predict 30-day mortality and readmission of in-hospital cardiac arrest, IHCA, survivors based on their historical claims using text 2 node to compute the distributed representations of all medical concept codes as a 128-dimensional vector. The results showed that our proposed approach could effectively alleviate data imbalance problems and train a better model for outcome prediction, achieving an area under the receiver operating characteristic of 0.752 for CA mortality, 0.711 for all mortality, 0.852 for CA readmission, and 0.889 for all readmission. This article was authored by Chi Niu Chi, Shuang A.O., Adrienne Winkler, and others.