Mai K T, Liu X T, Lin X Y, Liu S Y, Zhao C K, Du J B
The First Clinical Medical College, Nanjing Medical University, Nanjing 211166, China.
Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing 211166, China.
Zhonghua Liu Xing Bing Xue Za Zhi. 2024 Sep 10;45(9):1321-1326. doi: 10.3760/cma.j.cn112338-20240322-00148.
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.
基于人群的健康数据收集与分析在流行病学研究中具有重要意义。近年来，随着大数据、互联网和云计算的快速发展，人工智能逐渐引起了流行病学研究者的关注。越来越多的研究者尝试将人工智能算法用于基因组测序和医学图像数据挖掘，以及疾病诊断、风险预测等。近年来，作为人工智能一个分支的机器学习已在流行病学研究中得到广泛应用。本文总结了机器学习在流行病学应用中的关键领域和进展，回顾了机器学习的发展历程，分析了其在流行病学研究应用中的经典案例和当前面临的挑战，并介绍了机器学习和人工智能算法的当前应用场景及未来发展趋势，以期更好地挖掘我国海量医疗卫生数据的流行病学研究价值。
