%A Da Jingwei,Yan Jiaqi,Deng Sanhong,Wang Zhongmin %T Predicting Hospital Readmissions with Deep Learning: Case Study of Heart Diseases %0 Journal Article %D 2020 %J Data Analysis and Knowledge Discovery %R 10.11925/infotech.2096-3467.2020.0469 %P 63-73 %V 4 %N 11 %U {https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/abstract/article_4892.shtml} %8 2020-11-25 %X

[Objective] This paper uses the deep learning method to predict possible readmissions of patients based on their electronic medical records, aiming to improve hospital management. [Methods] We proposed a model based on character-level convolution neural network to process the unstructured texts. Then, with the help of structured data (demographics, clinical records and administrative data) to predict the hospital readmission cases. [Results] The deep learning model combining structured and unstructured data yielded better prediction results at F1-score of 0.735. Compared with the models only using structured or unstructured data, the F1-score was increased by 12.9% and 2.1%, respectively. [Limitations] The experimental medical records were collected from one hospital, which has some impacts on prediction results. [Conclusions] The proposed model provides references for researchers of hospital readmission prediction and hospital administrators.