Background Artificial intelligence (AI) holds the promise of supporting nurses' clinical
decision-making in complex care situations or conducting tasks that are remote from direct …

Osteoporosis and its clinical consequence, bone fracture, is a multifactorial disease that has
been the object of extensive research. Recent advances in machine learning (ML) have …

Predicting future blood glucose (BG) levels for diabetic patients will help them avoid
potentially critical health issues. We demonstrate the use of machine learning models to …

Nowadays, it becomes of paramount societal importance to support many frail-prone groups
in our society (elderly, patients with neurodegenerative diseases, etc.) to remain socially and …

To evaluate and holistically treat the mental health sequelae and potential psychiatric
comorbidities associated with obstetric and gynaecological conditions, it is important to …

Background Short-term fall prediction models that use electronic health records (EHRs) may
enable the implementation of dynamic care practices that specifically address changes in …

Background Falls and their consequences are a serious public health problem worldwide.
Each year, 37.3 million falls requiring medical attention occur. Therefore, the analysis of fall …

Background The use of artificial intelligence (AI) in the field of rehabilitation is growing
rapidly. Therefore, there is a need to understand how physical therapists (PTs) perceive AI …

Artificial intelligence (AI) has been used in physical therapy diagnosis and management for
various impairments. Physical therapists (PTs) need to be able to utilize the latest innovative …

Background Falls in older people are common and morbid. Prediction models can help
identifying individuals at higher fall risk. Electronic health records (EHR) offer an opportunity …