Abstract Machine learning is frequently being leveraged to tackle problems in the health
sector including utilization for clinical decision-support. Its use has historically been focused …

Abstract Machine learning (ML) and artificial intelligence (AI) algorithms have the potential to
derive insights from clinical data and improve patient outcomes. However, these highly …

Objective We extended a 2013 literature review on electronic health record (EHR) data
quality assessment approaches and tools to determine recent improvements or changes in …

Routine health care and research have been profoundly influenced by digital-health
technologies. These technologies range from primary data collection in electronic health …

Background The promise of digital health is principally dependent on the ability to
electronically capture data that can be analyzed to improve decision-making. However, the …

Investigation of the potential applications of artificial intelligence (AI), including machine
learning (ML) and deep learning (DL) techniques, is an exponentially growing field in …

Metformin, a diabetes drug with anti-aging cellular responses, has complex actions that may
alter dementia onset. Mixed results are emerging from prior observational studies. To …

Electronic Health Records (EHRs) play an important role in the healthcare system. However,
their complexity and vast volume pose significant challenges to data interpretation and …

Importance Psychiatric hospitalization is the standard of care for patients presenting to an
emergency department (ED) or urgent care (UC) with high suicide risk. However, the effect …

Artificial intelligence (AI) tools are increasingly able to learn from larger and more complex
data, thus allowing clinicians and scientists to gain new insights from the information they …