Abstract
With the development of medical informatization, electronic medical records are important in hospital information systems, and their use for patient mortality prediction can contribute to further improvement of clinical auxiliary diagnosis decision-making systems. Existing models for mortality risk prediction achieve good performance; however, data utilisation is limited predominantly to a single aspect. In this study, a scalable two-layer attention mechanism neural network is proposed to predict patient mortality by focusing on the patient diagnostic code, drug code, surgical code, and global condition. The first layer of the attention network uses three independent long short-term memory networks to learn the attention features of diagnosis, medication, and surgery of a patient single admission, and output three feature matrices. The feature matrix output of the first layer of the attention network is spliced and input for use, and the second layer of the attention network uses the transformer encoder. Finally, a fully connected layer is used to obtain the mortality prediction of the patient. The feasibility of the model is demonstrated by comparison with baseline methods and ablation experiments. In conclusion, the proposed model can comprehensively consider the information of patient diagnosis, medication, and surgery, and has good practicability and scalability.
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Wang, L. et al. (2023). Patient Mortality Prediction Based on Two-Layer Attention Neural Network. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science, vol 14088. Springer, Singapore. https://doi.org/10.1007/978-981-99-4749-2_20
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DOI: https://doi.org/10.1007/978-981-99-4749-2_20
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