Abstract
Nursing adverse event means an abnormal event in the process of nursing that causes or may cause adverse outcomes to patients and their families. Its ability to damage personal health or increase the economic burden of patients. At present, the analysis of nursing adverse event report mainly focuses on its structured report content. However, the unstructured text content in the report contains the whole process of the event, but it is often ignored. To tackle this problem, this study proposed a hybrid neural network model for adverse nursing event reports. It uses convolutional neural network and attention based short-term memory to extract text features respectively, and combines structured data. Finally, a feature fusion mechanism is proposed to fuse features at the same scale. To evaluate the proposed method, we constructed a private data set which contained 13265 reports of Chinese nursing adverse events, and compared our method with other currently popular methods. Experimental results show that the proposed model achieves 84.4% f-measure in this task. The comparison results of different models prove that our model is superior to the traditional statistical model, and has better effectiveness and applicability.
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Acknowledgments
This work was supported by the Key Project of Science and Technology Research of Henan Province (No. 222102210112), the National Natural and Science Fund of China (No. 61802350, 81971615), National Key Research and Development Program of China (No. 2019YFC0118803).
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Ge, X., Li, K., Ding, J., Li, F., Cheng, M. (2023). Automatic Classification of Nursing Adverse Events Using a Hybrid Neural Network Model. In: Tang, B., et al. Health Information Processing. CHIP 2022. Communications in Computer and Information Science, vol 1772. Springer, Singapore. https://doi.org/10.1007/978-981-19-9865-2_13
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DOI: https://doi.org/10.1007/978-981-19-9865-2_13
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