MIT researchers have identified significant examples of machine-learning model failure when those models are applied to data ...

Models using established cardiovascular disease risk factors had satisfactory predictive performance for 5-year CVD risk in ...

Depression is one of the most widespread mental health disorders worldwide, affecting approximately 4% of the global ...

Machine learning can help predict whether people newly diagnosed with MS will experience disability worsening that occurs ...

Researchers developed a machine learning model that could identify children in the ED who were at risk for developing sepsis ...

Disinfecting drinking water prevents the spread of deadly waterborne diseases by killing infectious agents such as bacteria, ...

In the past decade, cloud-scale analytics tools have transformed the digital fight against deforestation. Instead of manual ...

Background Annually, 4% of the global population undergoes non-cardiac surgery, with 30% of those patients having at least ...

When pitching the use of a model, data scientists rarely report on its potential value. They then experience an unnerving ...

Objective: This study compared a conventional logistic regression model with machine learning (ML) models using demographic and clinical data to predict outcomes at 2 and 6 months of treatment for MDR ...

Background: This study developed a machine learning model to predict postoperative heart failure (HF) risk in non-cardiac surgery patients. Methods: Using data from 489 patients (109 HF cases, 380 ...