Abstract：
Allergic diseases and asthma are intrinsically linked to the environment we live in and to patterns of exposure. The integrated approach to understanding the effects of exposures on the immune system includes the ongoing collection of large-scale and complex data. This requires sophisticated methods to take full advantage of what this data can offer. Here we discuss the progress and further promise of applying artificial intelligence and machine-learning approaches to help unlock the power of complex environmental data sets toward providing causality models of exposure and intervention. We discuss a range of relevant machine-learning paradigms and models including the way such models are trained and validated together with examples of machine learning applied to allergic disease in the context of specific environmental exposures as well as attempts to tie these environmental data streams to the full representative exposome. We also discuss the promise of artificial intelligence in personalized medicine and the methodological approaches to healthcare with the final AI to improve public health.
摘要：
过敏性疾病和哮喘与我们生活的环境和暴露模式有着内在的联系。了解暴露对免疫系统影响的综合方法包括持续收集大规模和复杂的数据。这需要复杂的方法来充分利用这些数据可以提供的东西。在这里，我们讨论了应用人工智能和机器学习方法来帮助释放复杂环境数据集提供暴露和干预因果关系模型的能力的进展和进一步的承诺。我们讨论了一系列相关的机器学习范例和模型，包括这些模型的训练和验证方式，以及在特定环境暴露的背景下应用于过敏性疾病的机器学习的例子，以及将这些环境数据流与完全有代表性的暴露结合起来的尝试。我们还讨论了人工智能在个性化医疗中的前景，以及医疗保健的方法学方法，最终人工智能改善了公众健康。
