Summary
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.
Key words Pervasive health - health data analytics - machine learning
