深度学习在健康医疗中的应用研究综述  

Review of Deep Learning Applications in Healthcare

作　　者：雪峰豪 蒋海波[2] 唐聃[1] XUE Fenghao;JIANG Haibo;TANG Dan(School of Software Engineering,Chengdu University of Information Technology,Chengdu 610225,China;Chengdu Institute of Biology,Chinese Academy of Sciences,Chengdu 610041,China)

机构地区：[1]成都信息工程大学软件工程学院,成都610225 [2]中国科学院成都生物研究所,成都610041

出　　处：《计算机科学》2023年第4期1-15,共15页Computer Science

基　　金：西部之光青年学者A类项目(2021XBZG-A-002)。

摘　　要：随着生物医学和信息技术的快速融合发展,健康医疗领域积累了海量的影像数据、患者报告数据、电子健康记录和组学数据等,这些数据具有复杂性、异构性和高维等特点。而深度学习有着复杂函数模拟和自动学习特征的能力,能够从复杂的数据中较为精准地提取有效的信息,可为医学诊断、药物研发等方面的研究提供高效的技术支撑。目前,深度学习在医学影像方面已经取得极大的成功,一些基于深度学习的医学影像诊断系统所获得的性能甚至能够与相关专家媲美。由于自然语言处理技术的进步,深度学习在利用非图像数据中的任务中也取得了显著的进步。文中首先简述了深度学习在健康医疗中的发展历程;然后,针对深度学习模型在健康医疗领域中的应用情况进行了统计分析,并对相关数据集进行了整理,还介绍了深度学习在疾病诊断、健康监护等医学诊疗过程中的研究情况,以及它在蛋白质结构预测和药物发现等方面的研究进展;最后,讨论了深度学习在健康医疗应用中存在的数据质量、可解释性、隐私安全和实际应用限制等关键挑战,以及应对这些挑战的可行方案或途径。With the rapid development and integration of biomedicine and information technology,massive amounts of imaging data,patient report data,electronic health records,and omics data have been accumulated rapidly in healthcare.These data are cha-racterized by complexity,heterogeneity and high dimensionality.Deep learning has the ability of complex function simulation and automatic feature learning,which can provide efficient technical support for research in medical diagnosis and drug development.Currently,deep learning has been extremely successful in medical imaging and further more,some medical imaging diagnostic systems based on deep learning have achieved performance that is even comparable to that of relevant experts.Due to the progress of natural language processing technology,deep learning has also made remarkable progress in the use of non-image data tasks.This paper first briefly describes the development of deep learning in healthcare.Subsequently,the application of deep learning model in healthcare is statistically analyzed,and some available datasets are sorted out.In addition,this paper also introduces the research progress of deep learning in medical diagnosis and treatment processes such as disease diagnosis and health monitoring,and its research progress in protein structure prediction and drug discovery.Finally,key challenges of deep learning in healthcare applications such as data quality,interpretability,privacy security and practical application limitations are discussed.It also discusses feasible solutions or approaches to these challenges.

关 键 词：深度学习 疾病诊断 健康监护 蛋白质结构预测 药物发现 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TP391.7[自动化与计算机技术—控制科学与工程]