非结构化数据表征增强的术后风险预测模型

An Unstructured Data Representation Enhanced Method for Postoperative Risk Prediction

准确的术后风险预测对临床资源的规划、应急方案的准备以及患者术后风险和死亡率的降低具有积极的作用。目前,术后风险预测主要基于患者的基本信息、术前的实验室检查及术中的生命体征等结构化数据,蕴含着丰富语义信息的非结构化术前诊断的价值尚待验证。针对上述问题,该文提出一种非结构化数据表征增强的术后风险预测模型,利用自注意力机制,将结构化数据与术前诊断进行信息加权融合。基于临床数据,该文将所提出的模型与术后风险预测常用的统计机器学习模型以及最新的深度神经网络进行对比,在肺部并发症风险预测、ICU入室风险预测和心血管不良风险预测任务上的F1值平均提升了9.533%,同时预测模型还具有良好的可解释性。

Postoperative risk prediction has a positive effect on clinical resource plan,emergency plan preparation and postoperative risk and mortality reduction.To employ the unstructured preoperative diagnosis with rich semantic information,this paper proposes a postoperative risk prediction model via unstructured data representation enhancement.The model utilizes self-attention to fuse the structured data with unstructured preoperative diagnosis.Compared with the baseline methods,the proposed model improves F 1-Score by an average of 9.533%on the tasks of the pulmonary complication risk prediction,the ICU admission risk prediction and the cardiovascular adverse risk prediction.