INTRODUCTION This accompanying editorial provides a brief introduction to this focus theme, focused on "Machine Learning and Data Analytics in Pervasive Health". OBJECTIVE The innovative use of machine learning technologies combining small and big data analytics will support a better provisioning of healthcare to citizens. This focus theme aims to present contributions at the crossroads of pervasive health technologies and data analytics as key enablers for achieving personalised medicine for diagnosis and treatment purposes. METHODS A call for paper was announced to all participants of the "11th International Conference on Pervasive Computing Technologies for Healthcare", to different working groups of the International Medical Informatics Association (IMIA) and European Federation of Medical Informatics (EFMI) and was published in June 2017 on the website of Methods of Information in Medicine. A peer review process was conducted to select the papers for this focus theme. RESULTS Four papers were selected to be included in this focus theme. The paper topics cover a broad range of machine learning and data analytics applications in healthcare including detection of injurious subtypes of patient-ventilator asynchrony, early detection of cognitive impairment, effective use of small data sets for estimating the performance of radiotherapy in bladder cancer treatment, and the use negation detection in and information extraction from unstructured medical texts. CONCLUSIONS The use of machine learning and data analytics technologies in healthcare is facing a renewed impulse due to the availability of large amounts and new sources of human behavioral and physiological data, such as that captured by mobile and pervasive devices traditionally considered as nonmainstream for healthcare provision and management.

中文翻译：

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普适健康中的机器学习和数据分析。

引言本文随附的社论简要介绍了这一主题，重点是“普及性健康中的机器学习和数据分析”。目标机器学习技术的创新使用，结合了大数据分析和小数据分析，将有助于更好地为公民提供医疗服务。该重点主题旨在介绍在普及型健康技术和数据分析的十字路口上做出的贡献，作为实现用于诊断和治疗目的的个性化药物的关键推动力。方法向“第十一届医疗普适计算技术国际会议”的所有参与者宣布征集论文，提交给国际医学信息学协会（IMIA）和欧洲医学信息学联合会（EFMI）的不同工作组，并于2017年6月发布在医学信息方法网站上。进行了同行评审过程，以选择该主题的论文。结果选择了四篇论文，将其纳入此重点主题。论文主题涵盖了医疗领域中广泛的机器学习和数据分析应用，包括检测患者-呼吸机异步性的有害亚型，及早发现认知障碍，有效利用小型数据集来评估放射治疗在膀胱癌治疗中的表现，以及在非结构化医学文本中使用否定检测和信息提取。