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数据分析与知识发现  2021, Vol. 5 Issue (8): 65-75     https://doi.org/10.11925/infotech.2096-3467.2021.0188
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
基于机器学习组合优化方法的术后感染预测模型研究*
苏强1,侯校理1(),邹妮2
1同济大学经济与管理学院 上海 200092
2上海交通大学附属第一人民医院 上海 200240
Predicting Surgical Infections Based on Machine Learning
Su Qiang1,Hou Xiaoli1(),Zou Ni2
1School of Economics and Management, Tongji University, Shanghai 200092, China
2Shanghai General Hospital, Shanghai 200240, China
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摘要 

【目的】 提高患者术后感染风险预测的准确性和有效性,探索基于机器学习组合预测方法建立术后感染预测模型。【方法】 首先选择SMOTE、ADASYN和随机过采样三种采样技术以平衡数据集,然后结合5种常用机器学习模型生成多种预测组合,提出基于改进人工蜂群算法的采样技术与预测模型的混合预测方法,最后验证并比较多种组合预测方法的有效性。【结果】 实证分析显示,采用人工蜂群算法组合策略方法下的混合模型的GM值和F1值分别达到0.791 2和0.669 3,相较于单一预测模型分别提升了15.15%和23.62%。【局限】 模型需要在更大的SSI数据集层面进一步验证。【结论】 基于人工蜂群组合优化方法的混合预测模型能够有效提高术后感染预测能力,尤其是对阳性患者的预测,为实际临床应用提供参考。

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苏强
侯校理
邹妮
关键词 手术部位感染预测组合人工蜂群算法过采样机器学习    
Abstract

[Objective] This paper proposes a prediction model for post-operative infection based on a combined machine learning algorithm, aiming to effectively reduce surgical site infection risks. [Methods] First, we used SMOTE, ADASYN, and random oversampling to reduce the imbalance of the original data. Then, we combined five commonly used predictive models: Lasso, SVM, GBDT, ANN and RF to create a hybrid prediction method. Finally, we used the improved artificial bee colony algorithm to optimize the weight of multiple combinations. [Results] The G-mean and F1 values of the ABC combination strategy method reached 0.791 2 and 0.669 3 respectively, which were 15.15% and 23.62% higher than the existing ones. [Limitations] The sample size used in the study needs to be expanded. [Conclusions] The proposed model can effectively predict post-operative infections.

Key wordsSurgical Site Infection    Forecast Combination    Artificial Bee Colony Algorithm    Oversampling    Machine Learning
收稿日期: 2021-03-01      出版日期: 2021-09-15
ZTFLH:  R619  
基金资助:*国家自然科学基金项目(71972146);*国家自然科学基金项目(71974127)
通讯作者: 侯校理 ORCID:0000-0003-3609-4734     E-mail: houxl@tongji.edu.cn
引用本文:   
苏强, 侯校理, 邹妮. 基于机器学习组合优化方法的术后感染预测模型研究*[J]. 数据分析与知识发现, 2021, 5(8): 65-75.
Su Qiang, Hou Xiaoli, Zou Ni. Predicting Surgical Infections Based on Machine Learning. Data Analysis and Knowledge Discovery, 2021, 5(8): 65-75.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2021.0188      或      http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2021/V5/I8/65
Fig.1  组合预测方法的框架
预测结果 真实类别
阳性 阴性
阳性 TP FP
阴性 FN TN
Table 1  混淆矩阵
变量选取 类别变量 连续变量
一般情况 性别、ICD诊断编码、入住ICU 年龄、BMI、术前住院时长、ICU住院天数
既往病史 高血压、糖尿病、恶性肿瘤、肿瘤转移、心梗史、COPD、肾病史、肝病史、吸烟史、饮酒史
术前状态 术前30天有腹水、术前输血史、术前机械通气 RBC总数、WBC、血红蛋白、血肌酐、尿素氮、总胆红素、ALT、AST、白蛋白、前白蛋白、空腹血糖
手术信息 二次手术、手术方式、手术类型、主刀医生、切口类型、造口、引流、引流管数、术后吸氧 手术时长、术后早期血糖
Table 2  SSI数据描述
Fig.2  SSI数据集特征
数据集名称 变量
个数
样本
总数
多数
类样本数
少数
类样本数
不平
衡比
Wisconsin 9 683 444 239 1.86
Abalone17VS78910 8 2 338 2 280 58 39.31
Table 3  KEEL数据集描述
采样方法 分类器 准确率 敏感性 特异性 精确率 AUC GM F1
/ Lasso 0.914 4 0.340 0 0.976 3 0.683 4 0.932 4 0.576 2 0.454 1
GBDT 0.900 8 0.500 0 0.944 1 0.590 2 0.877 8 0.687 1 0.541 4
SVM 0.898 8 0.340 0 0.959 1 0.492 3 0.896 3 0.571 1 0.402 2
ANN 0.873 6 0.480 0 0.916 1 0.533 4 0.849 4 0.663 1 0.505 3
RF 0.906 6 0.400 0 0.961 3 0.762 0 0.906 2 0.620 1 0.524 6
SMOTE Lasso 0.852 3 0.720 0 0.866 7 0.493 5 0.916 2 0.789 9 0.585 6
GBDT 0.897 0 0.660 0 0.922 6 0.528 7 0.907 7 0.780 3 0.587 1
SVM 0.844 5 0.680 0 0.862 4 0.473 8 0.893 6 0.765 8 0.558 4
ANN 0.875 6 0.500 0 0.916 1 0.566 0 0.850 6 0.676 8 0.530 9
RF 0.912 4 0.540 0 0.952 7 0.647 7 0.930 6 0.717 3 0.589 0
随机过采样 Lasso 0.851 9 0.720 0 0.859 5 0.459 5 0.913 8 0.786 7 0.560 9
GBDT 0.895 0 0.620 0 0.924 7 0.530 8 0.913 4 0.757 2 0.571 9
SVM 0.840 7 0.640 0 0.862 4 0.456 6 0.895 0 0.742 9 0.533 0
ANN 0.871 7 0.500 0 0.911 8 0.521 9 0.845 8 0.675 2 0.510 7
RF 0.908 5 0.500 0 0.952 7 0.669 9 0.930 6 0.690 2 0.572 6
ADASYN Lasso 0.848 4 0.720 0 0.862 3 0.413 6 0.918 5 0.788 0 0.525 4
GBDT 0.881 4 0.560 0 0.916 1 0.491 0 0.908 8 0.716 3 0.523 2
SVM 0.827 0 0.640 0 0.847 3 0.380 1 0.894 4 0.736 4 0.476 9
ANN 0.865 9 0.520 0 0.903 2 0.509 0 0.852 5 0.685 3 0.514 4
RF 0.902 7 0.540 0 0.941 9 0.613 4 0.930 0 0.713 2 0.574 3
Table 4  单个模型组合不同采样方法的结果(SSI数据)
采样方法 组合策略 准确率 敏感性 特异性 精确率 AUC GM F1
/ Mean 0.912 4 0.460 0 0.961 3 0.659 5 0.924 0 0.665 0 0.542 0
Median 0.912 4 0.420 0 0.965 6 0.595 8 0.904 4 0.636 8 0.492 7
ABC 0.929 9 0.440 0 0.982 8 0.790 3 0.934 5 0.657 6 0.565 3
SMOTE Mean 0.889 2 0.600 0 0.920 4 0.560 1 0.930 0 0.743 1 0.579 3
Median 0.893 1 0.640 0 0.920 4 0.568 4 0.926 3 0.767 5 0.602 1
ABC 0.898 9 0.660 0 0.924 7 0.613 8 0.930 2 0.781 2 0.636 1
随机过采样 Mean 0.887 2 0.620 0 0.916 1 0.548 0 0.914 9 0.753 7 0.581 8
Median 0.891 1 0.620 0 0.920 4 0.558 3 0.906 9 0.755 4 0.587 5
ABC 0.908 6 0.620 0 0.939 8 0.599 9 0.928 5 0.763 3 0.609 8
ADASYN Mean 0.875 6 0.620 0 0.903 2 0.525 7 0.925 9 0.748 3 0.569 0
Median 0.875 6 0.620 0 0.903 2 0.536 7 0.926 1 0.748 3 0.575 3
ABC 0.900 8 0.600 0 0.933 3 0.594 2 0.922 0 0.748 3 0.597 1
Table 5  单一采样方法下不同模型组合策略结果(SSI数据)
组合
策略
准确率 敏感性 特异性 精确率 AUC GM F1
Mean 0.891 1 0.640 0 0.918 3 0.675 3 0.935 1 0.766 6 0.657 2
Median 0.889 2 0.620 0 0.918 3 0.658 7 0.933 1 0.754 5 0.638 8
ABC 0.916 4 0.660 0 0.948 4 0.678 9 0.936 9 0.791 2 0.669 3
Table 6  混合模型组合优化结果(SSI数据)
采样方法 分类器 Wisconsin Abalone
准确率 AUC GM F1 准确率 AUC GM F1
/ Lasso 0.966 2 0.993 5 0.961 1 0.951 8 0.975 6 0.839 7 0.129 1 0.032 8
GBDT 0.957 4 0.989 5 0.950 3 0.939 2 0.964 1 0.826 2 0.485 0 0.259 9
SVM 0.963 3 0.993 5 0.961 0 0.948 9 0.975 2 0.940 3 0.000 0 \
ANN 0.945 6 0.978 0 0.940 4 0.922 2 0.972 2 0.931 2 0.536 5 0.390 5
RF 0.960 4 0.990 8 0.956 7 0.944 5 0.974 8 0.863 2 0.223 4 0.093 6
SMOTE Lasso 0.966 2 0.993 6 0.963 2 0.952 3 0.873 4 0.932 9 0.839 8 0.375 6
GBDT 0.961 8 0.990 3 0.957 8 0.946 5 0.920 9 0.869 8 0.748 5 0.394 6
SVM 0.960 4 0.993 0 0.957 8 0.944 9 0.883 7 0.947 2 0.846 8 0.357 5
ANN 0.944 1 0.968 9 0.940 3 0.920 3 0.905 5 0.921 2 0.820 1 0.435 7
RF 0.961 8 0.988 6 0.958 8 0.946 3 0.873 0 0.852 2 0.767 7 0.335 2
随机过采样 Lasso 0.969 1 0.994 2 0.966 5 0.956 4 0.868 7 0.932 1 0.846 7 0.382 1
GBDT 0.966 2 0.990 9 0.965 2 0.953 1 0.952 1 0.901 0 0.680 5 0.416 7
SVM 0.966 3 0.994 5 0.966 3 0.952 8 0.883 7 0.948 2 0.838 2 0.365 6
ANN 0.947 1 0.974 4 0.943 6 0.925 0 0.888 8 0.937 1 0.830 1 0.377 3
RF 0.966 3 0.991 8 0.965 2 0.952 9 0.915 3 0.860 9 0.734 0 0.293 8
ADASYN Lasso 0.966 2 0.993 4 0.968 2 0.953 1 0.871 3 0.933 3 0.848 0 0.367 8
GBDT 0.966 3 0.987 3 0.966 2 0.953 0 0.913 6 0.880 1 0.723 9 0.355 6
SVM 0.961 8 0.993 2 0.966 7 0.947 7 0.882 8 0.948 1 0.846 3 0.348 5
ANN 0.947 1 0.983 6 0.944 7 0.925 6 0.918 3 0.949 3 0.862 5 0.426 2
RF 0.967 8 0.989 6 0.968 4 0.955 2 0.863 6 0.858 2 0.771 8 0.322 3
Table 7  单个模型组合不同采样方法的结果(KEEL数据集)
采样方法 组合策略 Wisconsin Abalone
准确率 AUC GM F1 准确率 AUC GM F1
/ Mean 0.958 9 0.992 8 0.947 7 0.941 3 0.976 5 0.920 9 0.339 7 0.207 0
Median 0.964 7 0.993 6 0.954 8 0.949 9 0.976 5 0.922 8 0.285 3 0.151 5
ABC 0.960 4 0.993 2 0.954 9 0.944 9 0.975 2 0.931 9 0.364 7 0.230 4
SMOTE Mean 0.970 7 0.993 4 0.970 2 0.959 0 0.907 2 0.936 1 0.846 2 0.440 8
Median 0.970 7 0.993 2 0.968 5 0.959 0 0.899 1 0.926 3 0.851 3 0.421 5
ABC 0.970 7 0.994 2 0.970 6 0.959 0 0.901 2 0.941 4 0.861 9 0.421 8
随机过采样 Mean 0.964 7 0.992 2 0.961 2 0.949 9 0.912 3 0.939 1 0.839 6 0.440 5
Median 0.967 7 0.993 5 0.962 7 0.954 5 0.902 9 0.933 5 0.825 4 0.389 7
ABC 0.967 7 0.992 8 0.964 8 0.954 5 0.912 3 0.951 0 0.860 0 0.477 7
ADASYN Mean 0.967 7 0.992 5 0.971 2 0.954 9 0.905 9 0.942 1 0.835 9 0.397 1
Median 0.970 7 0.993 8 0.972 6 0.959 0 0.902 1 0.943 6 0.871 0 0.414 5
ABC 0.972 1 0.993 8 0.975 6 0.961 2 0.897 8 0.937 5 0.859 4 0.427 6
混合模型 Mean 0.960 4 0.992 5 0.957 0 0.944 3 0.956 0 0.944 8 0.780 9 0.448 9
Median 0.960 4 0.993 2 0.959 1 0.944 7 0.920 0 0.912 3 0.743 9 0.425 6
ABC 0.970 7 0.994 9 0.976 9 0.959 3 0.907 6 0.953 4 0.883 8 0.479 3
Table 8  不同策略下的模型组合结果(KEEL数据集)
Fig.3  不同组合策略在GM和F1指标上的排名
[1] Ke C Y, Jin Y, Evans H, et al. Prognostics of Surgical Site Infections Using Dynamic Health Data[J]. Journal of Biomedical Informatics, 2017, 65:22-33.
doi: 10.1016/j.jbi.2016.10.021
[2] de Lissovoy G, Fraeman K, Hutchins V, et al. Surgical Site Infection: Incidence and Impact on Hospital Utilization and Treatment Costs[J]. American Journal of Infection Control, 2009, 37(5):387-397.
doi: S0196-6553(09)00073-X pmid: 19398246
[3] Hedrick T L, Sawyer R G, Friel C M, et al. A Method for Estimating the Risk of Surgical Site Infection in Patients with Abdominal Colorectal Procedures[J]. Diseases of the Colon & Rectum, 2013, 56(5):627-637.
[4] Bilimoria K Y, Liu Y M, Paruch J L, et al. Development and Evaluation of the Universal ACS NSQIP Surgical Risk Calculator: A Decision Aid and Informed Consent Tool for Patients and Surgeons[J]. Journal of the American College of Surgeons, 2013, 217(5):833-842.
doi: 10.1016/j.jamcollsurg.2013.07.385 pmid: 24055383
[5] Amri R, Dinaux A M, Kunitake H, et al. Risk Stratification for Surgical Site Infections in Colon Cancer[J]. JAMA Surgery, 2017, 152(7):686-690.
doi: 10.1001/jamasurg.2017.0505
[6] Bergquist J R, Thiels C A, Etzioni D A, et al. Failure of Colorectal Surgical Site Infection Predictive Models Applied to an Independent Dataset: Do They Add Value or Just Confusion?[J]. Journal of the American College of Surgeons, 2016, 222(4):431-438.
doi: 10.1016/j.jamcollsurg.2015.12.034 pmid: 26847588
[7] Bartz-Kurycki M A, Charles G, Anderson K T, et al. Enhanced Neonatal Surgical Site Infection Prediction Model Utilizing Statistically and Clinically Significant Variables in Combination with a Machine Learning Algorithm[J]. American Journal of Surgery, 2018, 216(4):764-777.
doi: S0002-9610(18)30093-X pmid: 30078669
[8] Grundmeier R W, Rui X, Ross R K, et al. Identifying Surgical Site Infections in Electronic Health Data Using Predictive Models[J]. Journal of the American Medical Informatics Association, 2018, 25(9):1160-1166.
doi: 10.1093/jamia/ocy075 pmid: 29982511
[9] Kuo P J, Wu S C, Chien P C, et al. Artificial Neural Network Approach to Predict Surgical Site Infection after Free-Flap Reconstruction in Patients Receiving Surgery for Head and Neck Cancer[J]. Oncotarget, 2018, 9(17):13768-13782.
doi: 10.18632/oncotarget.v9i17
[10] Zhu M, Xia J, Jin X Q, et al. Class Weights Random Forest Algorithm for Processing Class Imbalanced Medical Data[J]. IEEE Access, 2018, 6:4641-4652.
doi: 10.1109/ACCESS.2018.2789428
[11] Guo X J, Yin Y L, Dong C L, et al. On the Class Imbalance Problem[C]// Proceedings of the 4th International Conference on Natural Computation. 2008: 192-201.
[12] He H B, Garcia E A. Learning from Imbalanced Data[J]. IEEE Transactions on Knowledge & Data Engineering, 2009, 21(9):1263-1284.
[13] Nekooeimehr I, Lai-Yuen S K. Adaptive Semi-unsupervised Weighted Oversampling (A-SUWO) for Imbalanced Datasets[J]. Expert Systems with Applications, 2015, 46:405-416.
doi: 10.1016/j.eswa.2015.10.031
[14] Rivera W A, Xanthopoulos P. A Priori Synthetic Over-sampling Methods for Increasing Classification Sensitivity in Imbalanced Data Sets[J]. Expert Systems with Applications, 2016, 66:124-135.
doi: 10.1016/j.eswa.2016.09.010
[15] Kourentzes N, Barrow D, Petropoulos F. Another Look at Forecast Selection and Combination: Evidence from Forecast Pooling[J]. International Journal of Production Economics, 2018, 209:226-235.
doi: 10.1016/j.ijpe.2018.05.019
[16] 李静, 刘潇, 王效俐. 邻域粗糙集融合网格搜索组合分类器的理财决策知识获取研究[J]. 数据分析与知识发现, 2019, 3(1):85-94.
[16] ( Li Jing, Liu Xiao, Wang Xiaoli. Financial Decision Knowledge Acquisition Based on Neighborhood Rough Set and Ensemble Classifiers with Grid Search[J]. Data Analysis and Knowledge Discovery, 2019, 3(1):85-94.)
[17] 单英浩, 付青, 耿炫, 等. 基于改进BP-SVM-ELM与粒子化SOM-LSF的微电网光伏发电组合预测方法[J]. 中国电机工程学报, 2016, 36(12):3334-3343.
[17] ( Shan Yinghao, Fu Qing, Geng Xuan, et al. Combined Forecasting of Photovoltaic Power Generation in Microgrid Based on the Improved BP-SVM-ELM and SOM-LSF with Particlization[J]. Proceedings of the CSEE, 2016, 36(12):3334-3343.)
[18] Blanc S M, Setzer T. When to Choose the Simple Average in Forecast Combination[J]. Journal of Business Research, 2016, 69(10):3951-3962.
doi: 10.1016/j.jbusres.2016.05.013
[19] 刘洋, 冯玉强, 邵真. 基于Bagging与决策树算法的在线拍卖成交价格预测模型[J]. 系统工程理论与实践, 2009, 29(12):134-140.
[19] ( Liu Yang, Feng Yuqiang, Shao Zhen. Online Auction Final Price Forecasting Model Based on Bagging and Decision Tree[J]. Systems Engineering-Theory & Practice, 2009, 29(12):134-140.)
[20] 杨贵军, 徐雪, 赵富强. 基于XGBoost算法的用户评分预测模型及应用[J]. 数据分析与知识发现, 2019, 3(1):118-126.
[20] ( Yang Guijun, Xu Xue, Zhao Fuqiang. Predicting User Ratings with XGBoost Algorithm[J]. Data Analysis and Knowledge Discovery, 2019, 3(1):118-126.)
[21] Karaboga D, Basturk B. A Powerful and Efficient Algorithm for Numerical Function Optimization: Artificial Bee Colony (ABC) Algorithm[J]. Journal of Global Optimization, 2007, 39(3):459-471.
doi: 10.1007/s10898-007-9149-x
[22] Horng M H. Multilevel Thresholding Selection Based on the Artificial Bee Colony Algorithm for Image Segmentation[J]. Expert Systems with Applications, 2011, 38(11):13785-13791.
[23] Gao W F, Sheng H L, Wang J, et al. Artificial Bee Colony Algorithm Based on Novel Mechanism for Fuzzy Portfolio Selection[J]. IEEE Transactions on Fuzzy Systems, 2019, 27(5):966-978.
doi: 10.1109/TFUZZ.91
[24] Wang J, Wang Z, Li X, et al. Artificial Bee Colony-based Combination Approach to Forecasting Agricultural Commodity Prices[J/OL]. International Journal of Forecasting, 2019. https://doi.org/10.1016/j.ijforecast.2019.08.006.
[25] Kiran M S, Hakli H, Gunduz M, et al. Artificial Bee Colony Algorithm with Variable Search Strategy for Continuous Optimization[J]. Information Sciences, 2015, 300:140-157.
doi: 10.1016/j.ins.2014.12.043
[26] 周志华. 机器学习[M]. 北京: 清华大学出版社, 2016: 171-172.
[26] ( Zhou Zhihua. Machine Learning[M]. Beijing: Tsinghua University Press, 2016: 171-172.)
[27] Alcalá-Fdez J, Fernández A, Luengo J, et al. KEEL Data-Mining Software Tool: Data Set Repository, Integration of Algorithms and Experimental Analysis Framework[J]. Journal of Multiple-Valued Logic and Soft Computing, 2011, 17:255-287.
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