Postoperative risk Prediction has a positive effect on clinical resource planning, emergency plan preparation and reducing postoperative risk and mortality. Postoperative risk prediction is mainly based on patient's basic information, laboratory tests, vital signs and other structured data, while the value of unstructured preoperative diagnosis with rich semantic information remains to be verified. Aiming at attempting this problem, an unstructured data representation enhanced postoperative risk prediction model is proposed in this paper. The model utilizes self-attention to fuse structured data with, preoperative diagnosis. Through comparing with the commonly used statistical machine learning models and the state-of-the-art deep neural networks, the proposed model has not only better prediction performance, but also better interpret ability.
关键词
文本数据；术后风险预测；自注意力机制；信息融合
Key words
文本数据；术后风险预测；自注意力机制；信息融合
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会议名称
Chinese national conference on computational linguistic
会议地点
Nanchang(CN)
会议母体文献
The 21st Chinese national conference on computational linguistic
NETL