基于多通道的ICU脑血管疾病死亡风险预测模型  

Multichannel ICU Cerebrovascular Disease Death Risk Prediction Model

作　　者：成亚鑫 李润知[2] 赵红领[2] CHENG Yaxin;LI Runzhi;ZHAO Hongling(School of Information Engineering,Zhengzhou University,Zhengzhou 450001,China;Collaborative Innovation Center for Internet Medical and Health Services,Zhengzhou University,Zhengzhou 450052,China)

机构地区：[1]郑州大学信息工程学院,郑州450001 [2]郑州大学互联网医疗与健康服务河南省协同创新中心,郑州450052

出　　处：《计算机工程与应用》2022年第17期270-276,共7页Computer Engineering and Applications

基　　金：河南省科技厅科技攻关项目(192102310215);河南省高等学校重点科研项目(20A520036,21A520044)。

摘　　要：死亡风险预测指根据病人临床体征监测数据来预测未来一段时间的死亡风险。对于ICU病患,通过死亡风险预测可以有针对性地对病人做出临床诊断,以及合理安排有限的医疗资源。基于临床使用的MEWS和Glasgow昏迷评分量表,针对ICU病人临床监测的17项生理参数,提出一种基于多通道的ICU脑血管疾病死亡风险预测模型。引入多通道概念应用于BiLSTM模型,用于突出每个生理参数对死亡风险预测的作用。采用Attention机制用于提高模型预测精度。实验数据来自MIMICⅢ数据库,从中提取3 080位脑血管疾病患者的16 260条记录用于此次研究,除了六组超参数实验之外,将所提模型与LSTM、Multichannel-BiLSTM、逻辑回归(logistic regression)和支持向量机(support vector machine, SVM)四种模型进行了对比分析,准确率Accuracy、灵敏度Sensitive、特异性Specificity、AUC-ROC和AUC-PRC作为评价指标,实验结果表明,所提模型性能优于其他模型,AUC值达到94.3%。Death risk prediction refers to the prediction of the risk of death for a period of time in the future based on the monitoring data of the patient’s clinical signs. For ICU patients, it is possible to make targeted clinical diagnosis for patients through the prediction of death risk, and rationally arrange limited medical resources. Based on the clinically used MEWS and Glasgow coma score scale, aiming at the 17 physiological parameters monitored by ICU patients, a multi-channel ICU cerebrovascular disease death risk prediction model is proposed. First, the concept of multi-channel is introduced and applied to the BiLSTM model to highlight the role of each physiological parameter in predicting the risk of death. Second,the Attention mechanism is used to improve the prediction accuracy of the model. The experimental data comes from the MIMIC Ⅲ database, from which 16 260 records of 3 080 patients with cerebrovascular diseases are extracted for this study. In addition to six sets of hyperparameter experiments, the proposed model is compared and analyzed with LSTM,Multichannel-BiLSTM, and logistic regression and support vector machine four models. Accuracy, Sensitive, Specificity,AUC-ROC and AUC-PRC are used as evaluation indicators. The experimental results show that the performance of the proposed model is better than other models, and the AUC value reaches 94.3%.

关 键 词：脑血管疾病 重症监护病房(ICU) 双向长短时记忆 多通道 注意力机制 死亡风险预测 

分 类 号：TP399[自动化与计算机技术—计算机应用技术]