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。
[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.
作者
苏强
侯校理
邹妮
Su Qiang;Hou Xiaoli;Zou Ni(School of Economics and Management,Tongji University,Shanghai 200092,China;Shanghai General Hospital,Shanghai 200240,China)
出处
《数据分析与知识发现》
CSSCI
CSCD
北大核心
2021年第8期65-75,共11页
Data Analysis and Knowledge Discovery
基金
国家自然科学基金项目(项目编号:71972146,71974127)的研究成果之一。
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
Surgical Site Infection
Forecast Combination
Artificial Bee Colony Algorithm
Oversampling
Machine Learning
