The growing healthcare industry is generating a large volume of useful data on patient
demographics, treatment plans, payment, and insurance coverage—attracting the attention …

Over the past decade we have witnessed the increasing sophistication of machine learning
algorithms applied in daily use from internet searches, voice recognition, social network …

Background Studies have demonstrated that the current US guidelines based on American
College of Cardiology/American Heart Association (ACC/AHA) Pooled Cohort Equations …

Objective To assess the consistency of machine learning and statistical techniques in
predicting individual level and population level risks of cardiovascular disease and the …

Aims In the era of personalized medicine, it is of utmost importance to be able to identify
subjects at the highest cardiovascular (CV) risk. To date, single biomarkers have failed to …

Background A male gender driven obesity paradox (improved survival for overweight/obese
patients compared to normal weight) was recently shown in melanoma in the context of …

The study recognized the worth of understanding the how's of handling censoring and
censored data in survival analysis and the potential biases it might cause if researchers fail …

OBJECTIVE To combine prospective cohort studies, by including HLA harmonization, and
estimate risk of islet autoimmunity and progression to clinical diabetes. RESEARCH …

Background: Several factors are associated with an increased risk of anterior cruciate
ligament (ACL) reconstruction revision. However, the ability to accurately translate these …

Background Early prediction of childhood type 1 diabetes reduces ketoacidosis at diagnosis
and provides opportunities for disease prevention. However, only highly efficient …