Abstract
With the adoption of electronic health records (EHR), deep learning technologies have the potential to employ the EHR data to assist experts in better understanding the complex mechanisms underlying the health and disease. Existing studies have made progress on the research of medication combination prediction from the medical data, but few of them take into account the prior medical knowledge. This paper proposes a PKANet model that integrates the prior medical knowledge into the deep learning architecture to predict the medication combination. The prior medical knowledge is calculated from the mapping relation between diagnoses and medications hidden in the EHR data. It can provide the heuristic medications to help the PKANet model learn optimal parameters. In order to predict the possible medication combination, the PKANet model utilizes attention neural networks to obtain the relationship between different elements in the medical sequence data. The experiment results have demonstrated that the proposed PKANet model outperforms the state-of-the-art baselines on evaluation metrics.
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Acknowledgements
This work was supported by the National Key R&D Program of China (No. 2020AAA0107700), National Natural Science Foundation of China (Nos. 61976181, 11931015), Key Technology Research and Development Program of Science and Technology Scientific and Technological Innovation Team of Shaanxi Province (No. 2020TD-013), the Science and Technology Support Program of Guizhou (No. QKHZC2021YB531) and Chongqing Graduate Student Research Innovation Project (No. CYS21115).
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Wang, H., Dong, X., Luo, Z., Zhu, J., Zhu, P., Gao, C. (2021). Medication Combination Prediction via Attention Neural Networks with Prior Medical Knowledge. In: Qiu, H., Zhang, C., Fei, Z., Qiu, M., Kung, SY. (eds) Knowledge Science, Engineering and Management. KSEM 2021. Lecture Notes in Computer Science(), vol 12817. Springer, Cham. https://doi.org/10.1007/978-3-030-82153-1_26
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