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Ma M, Hao X, Zhao J, Luo S, Liu Y, Li D. Predicting heart failure in-hospital mortality by integrating longitudinal and category data in electronic health records. Med Biol Eng Comput 2023:10.1007/s11517-023-02816-z. [PMID: 36959414 DOI: 10.1007/s11517-023-02816-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 03/02/2023] [Indexed: 03/25/2023]
Abstract
Heart failure is a life-threatening syndrome that is diagnosed in 3.6 million people worldwide each year. We propose a deep fusion learning model (DFL-IMP) that uses time series and category data from electronic health records to predict in-hospital mortality in patients with heart failure. We considered 41 time series features (platelets, white blood cells, urea nitrogen, etc.) and 17 category features (gender, insurance, marital status, etc.) as predictors, all of which were available within the time of the patient's last hospitalization, and a total of 7696 patients participated in the observational study. Our model was evaluated against different time windows. The best performance was achieved with an AUC of 0.914 when the observation window was 5 days and the prediction window was 30 days. Outperformed other baseline models including LR (0.708), RF (0.717), SVM (0.675), LSTM (0.757), GRU (0.759), GRU-U (0.766) and MTSSP (0.770). This tool allows us to predict the expected pathway of heart failure patients and intervene early in the treatment process, which has significant implications for improving the life expectancy of heart failure patients.
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Affiliation(s)
- Meikun Ma
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, China
- Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province, Taiyuan, 030024, China
- Technology Research Center of Spatial Information Network Engineering of Shanxi, Taiyuan, 030024, China
| | - Xiaoyan Hao
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Jumin Zhao
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, China
- Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province, Taiyuan, 030024, China
- Intelligent Perception Engineering Technology Center of Shanxi, Taiyuan, 030024, China
| | - Shijie Luo
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Yi Liu
- Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province, Taiyuan, 030024, China
- Technology Research Center of Spatial Information Network Engineering of Shanxi, Taiyuan, 030024, China
- College of Data Science, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Dengao Li
- Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province, Taiyuan, 030024, China.
- Technology Research Center of Spatial Information Network Engineering of Shanxi, Taiyuan, 030024, China.
- College of Data Science, Taiyuan University of Technology, Taiyuan, 030024, China.
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