中国血吸虫病防治杂志 ›› 2022, Vol. 34 ›› Issue (5): 445-.

• 特约综述 •    下一篇

人工智能助力热带传染病防控研究

施亮1,2,张键锋1,2,李伟1,2,杨坤1,2,3*   

  1. 1 江苏省血吸虫病防治研究所、国家卫生健康委员会寄生虫病预防与控制技术重点实验室、江苏省寄生虫与媒介控制技术重点实验室(江苏 无锡 214064);2 江南大学公共卫生研究中心(江苏 无锡 214064);3 南京医科大学公共卫生学院(江苏 南京 211166)
  • 出版日期:2022-11-23 发布日期:2022-11-23
  • 通讯作者: 杨坤,博士,研究员,博士生导师,江苏省血吸虫病防治研究所副所长、江苏省医学重点人才、江苏省第一批卫生拔尖人才、江苏省“333”高层次人才、江苏省“六大人才高峰”高层次人才,主要从事寄生虫病控制、空间流行病学及全球卫生等工作。主持国家自然科学基金、江苏省自然科学基金、江苏省社会发展项目等科研课题10余项;主编中英文学术专著5部;获省科技进步奖二等奖、中华医学科技奖二等奖等10余项科研成果。自2016年起,主持援桑给巴尔血吸虫病防治项目,开创了我国援助非洲公共卫生和血吸虫病控制新局面。
  • 作者简介:施亮,男,硕士,主管医师。研究方向:空间流行病学与机器学习
  • 基金资助:
    江苏省国际科技合作项目(BZ2020003);江苏省卫生健康委医学科研项目(M2021102);江苏省无锡市科技发展资金(Y20212048);江苏省无锡市卫生健康委科研项目(M202121);江苏省血地寄科研课题(X202105);江南大学公共卫生研究中心课题(JUPH201837, JUPH202008)

Artificial intelligence facilitates tropical infectious disease control and research

SHI Liang1, 2, ZHANG Jian⁃feng1, 2, LI Wei1, 2, YANG Kun1, 2, 3*   

  1. 1 Jiangsu Institute of Parasitic Diseases, National Health Commission Key Laboratory of Parasitic Disease Control and Prevention, Jiangsu Provincial Key Laboratory on Parasite and Vector Control Technology, Wuxi, Jiangsu 214064, China; 2 Public Health Research Center, Jiangnan University, Wuxi, Jiangsu 214064, China; 3 School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China
  • Online:2022-11-23 Published:2022-11-23

摘要: 自新型冠状病毒肺炎疫情发生以来,人工智能技术在热带传染病领域应用的先进性逐渐凸显。人工智能技术的应用对缓解疾病诊疗负担、降低疾病漏诊和误诊率、提升疾病监测预警能力、提高医药和疫苗研发效率等均具有显著成效。本文分析了人工智能在热带传染病防控研究中的应用现状,论述了人工智能在该领域疾病诊疗、监测预警、疫苗与药物挖掘、医疗与公共卫生服务和全球卫生治理中的重要价值。根据人工智能助力热带传染病防控面临着诊断单一和不准确、开放环境监测预警能力不佳、智能系统服务能力有限、大数据管理困难、模型可解释性较差等方面的难题,本文提出了加强多种热带传染病多模态智能诊断、重视开放环境下媒介生物和风险人群智能监测预警、加快智能防控系统研发、强化伦理安全、大数据管理与模型可解释性等发展建议。

关键词: 热带传染病, 人工智能, 机器学习, 深度学习, 公共卫生, 全球卫生

Abstract: Since the global pandemic of coronavirus disease 2019 (COVID⁃19) in late 2019, artificial intelligence technology has shown increasing values in the research and control of tropical infectious diseases. The introduction of artificial intelligence technology has shown remarkable effectiveness to reduce the diagnosis and treatment burdens, reduce missing diagnosis and misdiagnosis, improve the surveillance and forecast ability and enhance the medicine and vaccine development efficiency. This paper summarizes the current applications of artificial intelligence in tropical infectious disease control and research and discusses the important values of artificial intelligence in disease diagnosis and treatment, disease surveillance and forecast, vaccine and drug discovery, medical and public health services and global health governance. However, artificial intelligence technology suffers from problems of single and inaccurate diagnosis, poor disease surveillance and forecast ability in open environments, limited capability of intelligent system services, big data management and model interpretability. Hereby, we propose suggestions with aims to improve multimodal intelligent diagnosis of multiple tropical infectious diseases, emphasize intelligent surveillance and forecast of vectors and high⁃risk populations in open environments, accelerate the research and development of intelligent management system, strengthen ethical security, big data management and model interpretability.

Key words: Tropical infectious disease, Artificial intelligence, Machine learning, Deep learning, Public health, Global health

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