基于深度学习的抗菌药物耐药性分析研究  

Research on Antimicrobial Resistance Analysis Based on Deep Learning

作　　者：谢修娟[1] 顾兵 XIE Xiujuan;GU Bing(Department of Computer Engineering,Southeast University Chengxian College,Nanjing 210000,China;College of Medical Technology,Xuzhou Medical University,Xuzhou 221004,China)

机构地区：[1]东南大学成贤学院计算机工程系,江苏南京210000 [2]徐州医科大学医学技术学院,江苏徐州221004

出　　处：《湖南大学学报（自然科学版）》2021年第10期113-120,共8页Journal of Hunan University:Natural Sciences

基　　金：国家自然科学基金资助项目(81871734);江苏省青蓝工程资助项目(202004)。

摘　　要：细菌耐药性的日益加剧,以及目前的耐药性检测方法周期长等问题,给临床第一时间准确用药带来极大的挑战和困境.为此,本文将探索深度学习技术在抗菌药物耐药性预测中的应用,提出一种融合注意力机制的双通道卷积神经网络模型,通过上下两个通道对建模后的送检数据做不同粒度的特征提取,每个通道经过卷积和池化后引入注意力机制,聚焦重要的特征信息,而后将两个通道的特征进行融合,从而完成分类输出.将模型在某三甲医院细菌药敏检测历史数据集上,与多种不同方法进行对比实验,结果表明,本文所提出方法在分类准确度F值指标中平均实现20.35%的提升,同时在小样本分类上表现出更好的效果.The increasing drug resistance of bacteria,as well as the long cycle of current drug resistance testing methods,bring great challenges and difficulties to accurate drug use at the first time in clinic.Therefore,this paper will explore the application of deep learning technology in the prediction of antimicrobial resistance,and proposes a dual-channel convolution neural network model integrating attention mechanisms.Through the upper and lower channels,different granularity features are extracted from laboratory data after modeling.After convolution and pooling,an attention mechanism is introduced in each channel to focus on important feature information,and then the features of the two channels are fused to complete the classification output.The model is applied to the historical data set of bacterial drug sensitivity test in a tertiary hospital,and compared with other methods.The results show that the proposed method achieves an average improvement of 20.35%in F-value index of classification accuracy,and performs better in small sample classification.

关 键 词：深度学习 卷积神经网络 注意力机制 耐药性预测 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]