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- W4328005919 abstract "Medication prediction plays an important role in the intelligent clinical decision support systems, which could not only improve the healthcare efficiency, but also ensure the treatment quality especially for intensive care unit patients. However, it is challenging to develop such an intelligent application due to the longitudinal and heterogeneous nature of electronic medical records (EMR), including dynamic data (e.g., laboratory examination results) and the static information (e.g., demographics). To that end, in this paper, we propose a novel memory augmented heterogeneous information fusion network to capture the complicated correlations of heterogeneous medical records and concurrently integrate them into patient representation according to their respective importance. Specifically, a memory augmented heterogeneous LSTM is incorporated to capture the temporal correlations of laboratory examination results by a sequential learning network, and model the interactions between the memory databases of hidden states and the memory database of medication representations via a novel co-general attention mechanism. Afterward, a heterogeneous information fusion module is presented to integrate the obtained heterogeneous information representations and explore the contribution of each heterogeneous EMR data. Finally, we conduct the experiments on a real-world MIMIC-III dataset with 11 disease conditions, and find that the proposed prediction model can achieve the superior prediction performances." @default.
- W4328005919 created "2023-03-22" @default.
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- W4328005919 date "2022-08-01" @default.
- W4328005919 modified "2023-10-16" @default.
- W4328005919 title "Medication Prediction Using Memory Augmented Heterogeneous Information Fusion Network" @default.
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- W4328005919 doi "https://doi.org/10.1109/bigcom57025.2022.00037" @default.
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