Abstract Introduction According to the World Health Organization (WHO), over 130 million
people are in constant need of humanitarian assistance due to natural disasters, disease …

Artificial intelligence (AI)–based clinical decision support systems are gaining momentum by
relying on a greater volume and variety of secondary use data. However, the uncertainty …

Objective The lack of representative coronavirus disease 2019 (COVID-19) data is a
bottleneck for reliable and generalizable machine learning. Data sharing is insufficient …

Production lines in pharmaceutical manufacturing generate numerous heterogeneous data
sets from various embedded systems which control the multiple processes of medicine …

Abstract Background and objective Reusing Electronic Health Records (EHRs) for Machine
Learning (ML) leads on many occasions to extremely incomplete and sparse tabular …

Background Unexpected variability across healthcare datasets may indicate data quality
issues and thereby affect the credibility of these data for reutilization. No gold-standard …

Background Temporal variability in health-care processes or protocols is intrinsic to
medicine. Such variability can potentially introduce dataset shifts, a data quality issue when …

Veracity, one of the five V's used to describe big data, has received attention when it comes
to using electronic medical record data for research purposes. In this perspective article, we …

Chest X-ray images are useful for early COVID-19 diagnosis with the advantage that X-ray
devices are already available in health centers and images are obtained immediately. Some …

Background: There is increasing recognition that health care providers need to focus
attention, and be judged against, the impact they have on the health outcomes experienced …