Machine learning model improves transplant risk assessment for patients with myelofibrosis, helping clinicians make informed decisions, as per an expert. A new machine learning model has significantly ...

A machine-learning analysis of brain waves recorded during sleep may help identify people at high risk of developing dementia, according to a study led by UC San Francisco and Beth Israel Deaconess ...

My company, Kickfurther, has carved out a niche by connecting businesses in need of funding for their retail inventory with buyers of that inventory. A key component of this business model is the ...

A machine-learning study finds that "brain age" calculated from sleep EEG waves can predict dementia risk, with a 10-year age gap increasing risk by 40%.

Objective This study reviewed the current state of machine learning (ML) research for the prediction of sports-related injuries. It aimed to chart the various approaches used and assess their efficacy ...

A total of 590 patients were identified, 432 in the development set and 158 in the validation set. The median age was 51 years, and 55.8% (329 of 590) experienced grade 3 or 4 toxicity. The ...

Systemic sclerosis (SSc) is a severe autoimmune disease with complex genetic causes. Some genetic contributors have been identified, but others remain unknown, which has impeded development of ...

The frequency of substance use, early age of initiation, and cannabis-related memory impairments are among the primary factors contributing to driving under the influence, according to a new analysis ...

Sensor data from wearable devices analyzed over five years reveals walking and posture differences that predict fall risk in Parkinson’s patients. Study: Predicting future fallers in Parkinson’s ...

Scientists at the Baylor College of Medicine and collaborating institutions used complementary approaches that integrate exome sequencing and evolutionary action machine learning to identify protein ...