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[Progress in application of machine learning in epidemiology].
Mai, K T; Liu, X T; Lin, X Y; Liu, S Y; Zhao, C K; Du, J B.
Affiliation
  • Mai KT; The First Clinical Medical College, Nanjing Medical University, Nanjing 211166, China.
  • Liu XT; Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing 211166, China.
  • Lin XY; The First Clinical Medical College, Nanjing Medical University, Nanjing 211166, China.
  • Liu SY; The First Clinical Medical College, Nanjing Medical University, Nanjing 211166, China.
  • Zhao CK; School of Stomatology, Nanjing Medical University, Nanjing 211166, China.
  • Du JB; Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing 211166, China Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China State Key Laboratory of Reproductive Medicine and Offspring Health, Nanjing Medical University,
Zhonghua Liu Xing Bing Xue Za Zhi ; 45(9): 1321-1326, 2024 Sep 10.
Article in Zh | MEDLINE | ID: mdl-39307708
ABSTRACT
Population based health data collection and analysis are important in epidemiological research. In recent years, with the rapid development of big data, Internet and cloud computing, artificial intelligence has gradually attracted attention of epidemiological researchers. More and more researchers are trying to use artificial intelligence algorithms for genome sequencing and medical image data mining, and for disease diagnosis, risk prediction and others. In recent years, machine learning, a branch of artificial intelligence, has been widely used in epidemiological research. This paper summarizes the key fields and progress in the application of machine learning in epidemiology, reviews the development history of machine learning, analyzes the classic cases and current challenges in its application in epidemiological research, and introduces the current application scenarios and future development trends of machine learning and artificial intelligence algorithms for the better exploration of the epidemiological research value of massive medical health data in China.
Subject(s)

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Machine Learning Limits: Humans Country/Region as subject: Asia Language: Zh Journal: Zhonghua Liu Xing Bing Xue Za Zhi Year: 2024 Document type: Article Affiliation country: Country of publication:

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Machine Learning Limits: Humans Country/Region as subject: Asia Language: Zh Journal: Zhonghua Liu Xing Bing Xue Za Zhi Year: 2024 Document type: Article Affiliation country: Country of publication: