The fifteen articles in this special section focus on data science used in smart healthcare applications. A shift toward a data-driven socio-economic health model is occurring. This is the result of the increased volume, velocity and variety of data collected from the public and private sector in healthcare, and biology in general. In the past five-years, there has been an impressive development of computational intelligence and informatics methods for application to health and biomedical science. However, the effective use of data to address the scale and scope of human health problems has yet to realize its full potential. The barriers limiting the impact of practical application of standard data mining and machine learning methods have been inherent to the characteristics of health data. Besides the volume of the data (‘big data’), these are challenging due to their heterogeneity, complexity, variability and dynamic nature. Finally, data management and interpretability of the results have been limited by practical challenges in implementing new and also existing standards across the different health providers and research institutions. The scope of this Special issue is to discuss some of these challenges and opportunities in health and biological data science, with particular focus on the infrastructure, software, methods and algorithms needed to analyze large datasets in biological and clinical research.

中文翻译：

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客座社论智能医疗中的数据科学：挑战与机遇


本专题部分的 15 篇文章重点关注智能医疗应用中使用的数据科学。向数据驱动的社会经济健康模式的转变正在发生。这是从医疗保健和生物学领域的公共和私营部门收集的数据数量、速度和种类不断增加的结果。在过去的五年中，计算智能和信息学方法在健康和生物医学科学中的应用取得了令人印象深刻的发展。然而，有效利用数据来解决人类健康问题的规模和范围尚未充分发挥其潜力。限制标准数据挖掘和机器学习方法实际应用影响的障碍是健康数据的固有特征。除了数据量（“大数据”）之外，这些数据还因其异构性、复杂性、可变性和动态性而具有挑战性。最后，数据管理和结果的可解释性受到不同卫生服务提供者和研究机构实施新标准和现有标准的实际挑战的限制。本特刊的范围是讨论健康和生物数据科学领域的一些挑战和机遇，特别关注分析生物和临床研究中的大型数据集所需的基础设施、软件、方法和算法。