Quistberg D Alex, Mooney Stephen J, Tasdizen Tolga, Arbelaez Pablo, Nguyen Quynh C
Urban Health Collaborative, Dornsife School of Public Health, Drexel University, Philadelphia, PA 19104, United States.
Department of Environmental and Occupational Health, Dornsife School of Public Health, Drexel University, Philadelphia, PA 19104, United States.
Am J Epidemiol. 2025 Feb 5;194(2):322-326. doi: 10.1093/aje/kwae215.
Deep learning is a subfield of artificial intelligence and machine learning, based mostly on neural networks and often combined with attention algorithms, that has been used to detect and identify objects in text, audio, images, and video. Serghiou and Rough (Am J Epidemiol. 2023;192(11):1904-1916) presented a primer for epidemiologists on deep learning models. These models provide substantial opportunities for epidemiologists to expand and amplify their research in both data collection and analyses by increasing the geographic reach of studies, including more research subjects, and working with large or high-dimensional data. The tools for implementing deep learning methods are not as straightforward or ubiquitous for epidemiologists as traditional regression methods found in standard statistical software, but there are exciting opportunities for interdisciplinary collaboration with deep learning experts, just as epidemiologists have with statisticians, health care providers, urban planners, and other professionals. Despite the novelty of these methods, epidemiologic principles of assessing bias, study design, interpretation, and others still apply when implementing deep learning methods or assessing the findings of studies that have used them.
深度学习是人工智能和机器学习的一个子领域，主要基于神经网络，且常与注意力算法相结合，已被用于在文本、音频、图像和视频中检测和识别对象。塞尔吉乌和拉夫（《美国流行病学杂志》。2023年；192(11):1904 - 1916）为流行病学家介绍了深度学习模型入门知识。这些模型为流行病学家提供了大量机会，通过扩大研究的地理范围、纳入更多研究对象以及处理大型或高维数据，来扩展和加强他们在数据收集和分析方面的研究。对于流行病学家来说，实施深度学习方法的工具不像标准统计软件中的传统回归方法那样直接或普遍可用，但与深度学习专家进行跨学科合作有令人兴奋的机会，就像流行病学家与统计学家、医疗保健提供者、城市规划师及其他专业人员合作一样。尽管这些方法新颖，但在实施深度学习方法或评估使用了这些方法的研究结果时，评估偏差、研究设计、解释等流行病学原则仍然适用。
