用机器学习分析复杂流行病数据，提升疾病预测与洞察力
Machine Learning in Epidemiology
- 结合监督与无监督学习，构建流行病数据分析框架
- 通过心臟病数据集验证模型效果，支持可解释性分析
- 适合需要处理高维健康数据的研究人员参考
在数字流行病学时代，流行病学家面临日益增长且维度复杂的海量数据。机器学习是一套强大的工具，可用于分析此类大规模数据。本章建立了在流行病学中成功应用机器学习的方法论基础，涵盖监督学习与无监督学习的基本原理，并讨论了最重要的机器学习方法。还发展了模型评估策略与超参数优化方法，并引入可解释机器学习。所有理论内容均配有R语言代码示例，全章使用一个关于心脏病的示例数据集。
原文摘要 · Abstract (English)
In the age of digital epidemiology, epidemiologists are faced by an increasing amount of data of growing complexity and dimensionality. Machine learning is a set of powerful tools that can help to analyze such enormous amounts of data. This chapter lays the methodological foundations for successfully applying machine learning in epidemiology. It covers the principles of supervised and unsupervised learning and discusses the most important machine learning methods. Strategies for model evaluation and hyperparameter optimization are developed and interpretable machine learning is introduced. All these theoretical parts are accompanied by code examples in R, where an example dataset on heart disease is used throughout the chapter.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。