Oliver Nuria, Mayora Oscar, Marschollek Michael
Methods Inf Med. 2018 Sep;57(4):194-196. doi: 10.1055/s-0038-1673243. Epub 2019 Jan 24.
This accompanying editorial provides a brief introduction to this focus theme, focused on "Machine Learning and Data Analytics in Pervasive Health".
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.
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.
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.
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.
这篇随附的社论简要介绍了这个重点主题，该主题聚焦于“普适健康中的机器学习与数据分析”。
将机器学习技术与大数据和小数据分析相结合的创新应用，将有助于为公民提供更好的医疗保健服务。这个重点主题旨在展示在普适健康技术与数据分析交叉领域的贡献，这些领域是实现用于诊断和治疗目的的个性化医疗的关键推动因素。
向“第11届医疗保健普适计算技术国际会议”的所有参与者、国际医学信息学协会（IMIA）和欧洲医学信息学联合会（EFMI）的不同工作组发出了征文通知，并于2017年6月在《医学信息方法》网站上发布。通过同行评审过程来挑选本重点主题的论文。
四篇论文被选入本重点主题。论文主题涵盖了机器学习和数据分析在医疗保健中的广泛应用，包括检测患者 - 呼吸机不同步的有害亚型、认知障碍的早期检测、有效利用小数据集评估膀胱癌放射治疗的效果，以及在非结构化医学文本中进行否定检测和信息提取。
由于大量新的人类行为和生理数据的出现，如传统上被视为医疗保健提供和管理非主流的移动和普适设备所捕获的数据，机器学习和数据分析技术在医疗保健中的应用正面临新的推动。
