Background The number of studies applying machine learning (ML) to predict acute kidney
injury (AKI) has grown steadily over the past decade. We assess and critically appraise the …

With recent advances in digitisation and big data analytics, more pharmaceutical firms are
adopting digital tools to achieve modernisation. The biological phenomena within …

Osteoporotic fractures (OF) are a global public health problem currently. Many risk prediction
models for OF have been developed, but their performance and methodological quality are …

Abstract Background The Kidney Failure Risk Equation (KFRE) predicts the 2-and 5-year
risk of end-stage renal disease (ESRD) in patients with chronic kidney disease (CKD) stages …

Background Since diagnosis of mood disorder heavily depends on signs and symptoms,
emerging researches have been studying biomarkers with the attempt to improve diagnostic …

Background Some researchers have used machine learning to predict mortality in old
patients with hip fracture, but its application value lacks an evidence-based basis. Hence …

Kidney transplant recipients and transplant physicians face important clinical questions
where machine learning methods may help improve the decision-making process. This mini …

Objectives The aim of this study is to investigate risk factors for external root resorption
(ERR) of second molars (M2) associated with impacted third molars (M3), and to develop a …

Objectives Invasive fungal super-infection (IFSI) is an added diagnostic and therapeutic
dilemma. We aimed to develop and assess a nomogram of IFSI in patients with healthcare …

Background. Recurrent membranous nephropathy (MN) posttransplantation affects 35% to
50% of kidney transplant recipients (KTRs) and accounts for 50% allograft loss 5 y after …