The American journal of sports medicine
Dec 23, 2019
BACKGROUND: Hip arthroscopy has become an important tool for surgical treatment of intra-articular hip pathology. Predictive models for clinically meaningful outcomes in patients undergoing hip arthroscopy for femoroacetabular impingement syndrome (F...
OBJECTIVE: We aimed to investigate the association between sleep HRV and long-term cardiovascular disease (CVD) outcomes, and further explore whether HRV features can assist the automatic CVD prediction.
Diabetes is a large healthcare burden worldwide. There is substantial evidence that lifestyle modifications and drug intervention can prevent diabetes, therefore, an early identification of high risk individuals is important to design targeted preven...
BACKGROUND: Body composition is increasingly being recognized as an important prognostic factor for health outcomes across cancer, liver cirrhosis, and critically ill patients. Computed tomography (CT) scans, when taken as part of routine care, provi...
Application of machine learning techniques has the potential to yield unseen insights into movement and permits visualisation of complex behaviours and tangible profiles. The aim of this study was to identify profiles of relative motor competence (MC...
BACKGROUND: Atrial fibrillation (AF) is the most common sustained heart arrhythmia. However, as many cases are asymptomatic, a large proportion of patients remain undiagnosed until serious complications arise. Efficient, cost-effective detection of t...
Obesity is associated with changes in the plasma lipids. Although simple lipid quantification is routinely used, plasma lipids are rarely investigated at the level of individual molecules. We aimed at predicting different measures of obesity based on...
BACKGROUND: Diabetes Mellitus is an increasingly prevalent chronic disease characterized by the body's inability to metabolize glucose. The objective of this study was to build an effective predictive model with high sensitivity and selectivity to be...
Artificial intelligence (AI) will pave the way to a new era in medicine. However, currently available AI systems do not interact with a patient, e.g., for anamnesis, and thus are only used by the physicians for predictions in diagnosis or prognosis. ...
BACKGROUND: We aimed to demonstrate that supervised machine learning (ML) models can better predict postoperative complications after total shoulder arthroplasty (TSA) than comorbidity indices.