摘要
医学信息化的高速发展和人工智能技术的不断革新,使得充分利用数据分析或挖掘方法对医学数据进行分析和预测成为可能,这不仅可为患者提供更加精准的诊断和治疗依据,也可为政府和医院合理调配医疗卫生资源提供重要决策参考。长短期记忆网络作为机器学习方法中处理时间序列数据的经典模型,能够突破统计学的一些局限而处理规模较为庞大且复杂的医学时间序列数据。目前长短期记忆网络在医药卫生和生物医学领域的应用主要涉及7个应用主题,包括自然语言处理与文本挖掘、生物医学信息、信号类、运动、临床病历、医院管理、公共卫生及政策。
The rapid development of medical informatization and continuous innovation of artificial intelligence have made it possible to analyze data and predict prognosis through making full use of data analysis or data mining methods in medical field,which can provide not only more accurate basis of diagnosis and treatment for patients but also important decision-making reference for the government and hospitals to allocate medical resources reasonably.As a classical model for processing time series data in machine learning,long short-term memory network can break through some limitations of statistics to process large and complex medical data.The current applications of long short-term memory networks in medical and biomedical fields can be mainly summarized as seven themes,including natural language processing,biomedical information,signals,motion,clinical medical records,hospital management,and public health and policy.
作者
曾瑜
杨晓妍
张伟
ZENG Yu;YANG Xiaoyan;ZHANG Wei(West China Biomedical Big Data Center,West China Hospital/West China School of Medicine,Sichuan University,Chengdu,Sichuan 610041,P.R.China)
出处
《华西医学》
CAS
2021年第1期131-136,共6页
West China Medical Journal
基金
四川省重点研发项目(2021YFS0091)
成都市科学技术局重点研发支撑计划(2019-YF09-00088-SN)。
关键词
长短期记忆网络
医学
公共卫生
生物医学
Long short-term memory network
Medicine
Public health
Biomedicine
