Asthma is a common chronic health condition affecting millions of people in the United States. While asthma cannot be cured, it can be managed if we identify and understand triggers and risk factors that cause asthma exacerbations. However, this is challenging because these triggers and risk factors are complex and interconnected, and there are limitations to current mainstream approaches for identifying them. The recent availability of massive amounts of heterogeneous data has opened up new possibilities for asthma triggers and risk factors analyses. In this study, we introduce a data-driven framework, adapt and integrate multiple advanced machine learning techniques, and perform an empirical analysis to (1) derive characteristics of self-reported asthma patients from social media, (2) enable integration and repurposing of highly heterogeneous and commonly available datasets, and (3) uncover the sequential patterns of asthma triggers and risk factors, and their relative importance, both of which are difficult to achieve via retrospective cohort-based studies. Our methods and results can provide guidance for developing asthma management plans and interventions for specific subpopulations and, eventually, have the potential to reduce the societal burden of asthma.

中文翻译：

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基于机器学习和大型异构数据源的哮喘诱发因素和危险因素的综合分析

哮喘是一种常见的慢性健康状况，在美国影响数百万人。尽管哮喘无法治愈，但如果我们识别并了解导致哮喘加重的诱因和危险因素，就可以对付哮喘。但是，这具有挑战性，因为这些触发因素和风险因素是复杂且相互关联的，并且当前用于识别它们的主流方法存在局限性。最近可获得的大量异构数据为哮喘触发因素和危险因素分析开辟了新的可能性。在这项研究中，我们引入了一个数据驱动的框架，适应并整合了多种先进的机器学习技术，并进行了一项实证分析，以（1）从社交媒体中得出自我报告的哮喘患者的特征，（2）实现高度异质且通用的数据集的整合和再利用，（3）揭示哮喘触发因素和危险因素的顺序模式及其相对重要性，这两者均难以通过基于回顾性队列研究的方法来实现。我们的方法和结果可为制定哮喘管理计划和针对特定亚人群的干预措施提供指导，并最终有可能减轻哮喘的社会负担。