引用本文
  • 顾坚磊,江建平,田园,蔡小舒,吕晖,于广军.人工智能技术的应用:罕见病临床决策系统的需求、现状与挑战[J].第二军医大学学报,2018,39(8):819-825    [点击复制]
  • GU Jian-lei,JIANG Jian-ping,TIAN Yuan,CAI Xiao-shu,LÜ Hui,YU Guang-jun.Application of artificial intelligence technology: clinical demands, status and challenges of clinical decision support system for rare diseases[J].Acad J Sec Mil Med Univ,2018,39(8):819-825   [点击复制]
【打印本页】 【下载PDF全文】 【HTML】 查看/发表评论下载PDF阅读器关闭

←前一篇|后一篇→

过刊浏览    高级检索

本文已被:浏览 800次   下载 700 本文二维码信息
码上扫一扫!
人工智能技术的应用:罕见病临床决策系统的需求、现状与挑战
顾坚磊1,2,3△,江建平1,2△,田园3,4,蔡小舒1,3,吕晖1,2,3,于广军1,3*
0
(1. 上海交通大学附属儿童医院生物医学信息研究中心, 上海 200040;
2. 上海交通大学生命科学技术学院上海交大-耶鲁大学生物统计与数据科学联合中心, 上海 200240;
3. 上海交通大学附属儿童医院儿童精准医学大数据工程技术研究中心, 上海 200040;
4. 上海交通大学附属儿童医院儿童保健科, 上海 200040
共同第一作者
*通信作者)
摘要:
罕见病的发病率极低,但其总体患病人数不少。罕见病引起的病变后果严重,对患者及其家庭和社会造成了沉重负担。虽然当前利用基因测序技术、临床决策支持系统结合人工智能技术辅助临床进行遗传病诊断的研究火热,但临床诊断罕见病仍是非常大的技术挑战。本文简要综述了罕见病临床决策系统,旨在分析人工智能技术在罕见病中的发展现状和挑战。
关键词:  人工智能  罕见病  遗传性疾病  临床决策支持系统  知识库
DOI:10.16781/j.0258-879x.2018.08.0819
投稿时间:2018-08-06修订日期:2018-08-15
基金项目:上海市科学技术委员会2018年度上海市优秀学术技术带头人计划(18XD1403200),上海儿童精准医学大数据工程技术研究中心项目,上海市科学技术委员会专项基金(17DZ22512000),上海市卫生计划生育委员会研究课题(2018ZHYL0223),上海市卫生计划生育委员会青年研究项目(20144Y0179).
Application of artificial intelligence technology: clinical demands, status and challenges of clinical decision support system for rare diseases
GU Jian-lei1,2,3△,JIANG Jian-ping1,2△,TIAN Yuan3,4,CAI Xiao-shu1,3,LÜ Hui1,2,3,YU Guang-jun1,3*
(1. Biomedical Informatics Research Center, Children's Hospital of Shanghai Jiao Tong University, Shanghai 200040, China;
2. SJTU-Yale Joint Center of Biostatistics and Data Science, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China;
3. Big Data Engineering and Technology Research Center for Pediatric Precision Medicine, Children's Hospital of Shanghai Jiao Tong University, Shanghai 200040, China;
4. Department of Child Healthcare, Children's Hospital of Shanghai Jiao Tong University, Shanghai 200040, China
Co-first authors.
* Corresponding author)
Abstract:
The incidence of rare diseases is extremely low, but the overall number of patients with rare diseases is quite large. The consequences of rare diseases are severe and impose a heavy burden on patients, their families and the entire society. Although there are many researches on gene sequencing technology and clinical decision support system (CDSS) combined with artificial intelligence technology to assist the diagnosis of rare diseases, the diagnosis of rare diseases remains a great challenge in clinical practice. In this paper, we briefly reviewed the CDSS for rare diseases, aiming to analyze the current technique status and challenges of artificial intelligence technology in rare diseases.
Key words:  artificial intelligence  rare diseases  genetic diseases  clinical decision support systems  knowledge bases