邀请评论:深度学习方法扩大流行病学数据收集和分析
D Alex Quistberg1,2, Stephen J Mooney3, Tolga Tasdizen4,5
1Urban Health Collaborative, Dornsife School of Public Health, Drexel University, Philadelphia, PA 19104, United States.
American journal of epidemiology
|July 16, 2024
概括
深度学习是一种人工智能,为流行病学家提供了强大的工具,以扩大研究范围和数据分析能力. 应用这些先进的机器学习模型需要仔细考虑既定的流行病学原则.
科学领域:
- 流行病学 流行病学
- 人工智能的人工智能
- 机器学习 机器学习
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 深度学习,利用神经网络和注意力算法,越来越多地应用于各种数据类型 (文本,音频,图像,视频).
- 塞尔吉奥和鲁夫 (Am J Epidemiol. 一本由塞尔吉奥和鲁夫撰写的基础书. 2023) 将向流行病学家介绍深度学习模型.
- 流行病学家传统上使用统计软件,但深度学习提供了新的分析途径.
研究的目的:
- 为流行病学家提供深度学习模型的理解.
- 突出深度学习为流行病学研究提供的机会.
- 在使用深度学习时强调核心流行病学原则的持续相关性.
主要方法:
- 摘要讨论了深度学习模型,这是人工智能和机器学习的一个子领域.
- 它引用了一本流行病学家深度学习的入门书.
- 它涉及这些模型在数据收集和分析中的应用.
主要成果:
- 深度学习模型可以显著扩大研究范围,科目数量,以及处理大,高维数据的能力.
- 深度学习的实施工具不如传统的统计方法那么无处不在.
- 鼓励与深度学习专家进行跨学科的合作.
结论:
- 深度学习为推进流行病学研究提供了大量机会.
- 虽然新,但深度学习在流行病学中的应用需要遵守诸如偏见评估和研究设计等基本原则.
- 合作是有效利用这些先进的计算工具的关键.
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