Abstract
A hospital readmission risk prediction model based on electronic health record (EHR) data can be an important tool for identifying high-risk patients in need of additional support. Performant readmission models based on deep learning approaches require large, high-quality training datasets to perform optimally. Utilizing EHR data from a source hospital system to enhance prediction on a target hospital using traditional approaches might bias the dataset if distributions of the source and target data are different. There is a lack of an end-to-end readmission model that can capture cross-domain knowledge. Herein, we propose an early readmission risk temporal deep adaptation network, ERR-TDAN, for cross-domain spatial knowledge transfer. ERR-TDAN transforms source and target data to a common embedding space while capturing temporal dependencies of the sequential EHR data. Domain adaptation is then applied on a domain-specific fully connected linear layer. The model is optimized by a loss function that combines distribution discrepancy loss to match the mean embeddings of the two distributions and the task loss to optimize predicting readmission at the target hospital. In a use case of patients with diabetes, a model developed using target data of 37,091 patients from an urban academic hospital was enhanced by transferring knowledge from high-quality source data of 20,471 patients from a rural academic hospital. The proposed method yielded a 5% increase in F1-score compared to baselines. ERR-TDAN may be an effective way to increase a readmission risk model’s performance when data from multiple sites are available.
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This research was supported by the National Health Institute (NIH) under grant number R01DK122073.
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Abdel Hai, A. et al. (2023). Spatial Knowledge Transfer with Deep Adaptation Network for Predicting Hospital Readmission. In: Juarez, J.M., Marcos, M., Stiglic, G., Tucker, A. (eds) Artificial Intelligence in Medicine. AIME 2023. Lecture Notes in Computer Science(), vol 13897. Springer, Cham. https://doi.org/10.1007/978-3-031-34344-5_17
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DOI: https://doi.org/10.1007/978-3-031-34344-5_17
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