Periodic maintenance is common too, but still inefficient and often based on time, not actual machine condition. That ...

As organizations integrate data-driven insights into their operations, predictive screening models are emerging as both a ...

Objective To develop and validate a 10-year predictive model for cardiovascular and metabolic disease (CVMD) risk using ...

Individual prediction uncertainty is a key aspect of clinical prediction model performance; however, standard performance metrics do not capture it. Consequently, a model might offer sufficient ...

Based on findings published in NPJ Digital Medicine, the DT-GPT model outperformed 14 other forecasting models, including ...

CKM syndrome, which links heart disease, kidney disease, diabetes, and obesity, is a fairly new concept. As a result, ...

Journalism’s future will depend on whether institutions can adapt to, and meaningfully govern, the use of AI. That means not only developing new editorial standards and verification practices, but ...

Machine learning is increasingly recognized as a pivotal tool in the evolution of cardiovascular medicine, promising to ...

Pharmacogenomics: Another interesting impact of genetic variations is that it also influences how patients metabolize ...

Regulatory risks are also increasing. AI-enabled monitoring spans national borders, third-party vendors and multiple data processing intermediaries. As global regulations evolve, healthcare ...

Objective To develop prediction models for short-term outcomes following a first acute myocardial infarction (AMI) event (index) or for past AMI events (prevalent) in a national primary care cohort.