In the era of big data, standard analysis tools may be inadequate for making inference and there is a growing need for more efficient and innovative ways to collect, process, analyze and interpret the massive and complex data. We provide an overview of challenges in big data problems and describe how innovative analytical methods, machine learning tools and metaheuristics can tackle general healthcare problems with a focus on the current pandemic. In particular, we give applications of modern digital technology, statistical methods, data platforms and data integration systems to improve diagnosis and treatment of diseases in clinical research and novel epidemiologic tools to tackle infection source problems, such as finding Patient Zero in the spread of epidemics. We make the case that analyzing and interpreting big data is a very challenging task that requires a multi-disciplinary effort to continuously create more effective methodologies and powerful tools to transfer data information into knowledge that enables informed decision making.
翻译:在海量数据时代,标准分析工具可能不足以作出推断,而且越来越需要更有效和创新的方法来收集、处理、分析和解释大规模和复杂的数据,我们概述了海量数据问题的挑战,并描述了创新的分析方法、机器学习工具和计量经济学如何能解决一般保健问题,重点是目前的大流行病,特别是,我们运用现代数字技术、统计方法、数据平台和数据整合系统,在临床研究中改进疾病的诊断和治疗,并采用新的流行病学工具处理感染源问题,例如发现流行病蔓延中的零病人。我们证明,分析和解释海量数据是一项非常具有挑战性的任务,需要多学科的努力,不断创造更有效的方法和强有力的工具,将数据信息转化为知识,以便作出知情的决策。