Predicting Hospital Readmissions with Deep Learning: Case Study of Heart Diseases
Da Jingwei1,Yan Jiaqi1(),Deng Sanhong1,2,Wang Zhongmin3
1School of Information Management, Nanjing University, Nanjing 210023, China 2Jiangsu Key Laboratory of Data Engineering and Knowledge Service, Nanjing 210023, China 3Jiangsu Province Hospital (The First Affiliated Hospital of Nanjing Medical University), Nanjing 210029, China
[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.
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Da Jingwei,Yan Jiaqi,Deng Sanhong,Wang Zhongmin. Predicting Hospital Readmissions with Deep Learning: Case Study of Heart Diseases. Data Analysis and Knowledge Discovery, 2020, 4(11): 63-73.
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