Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered ...
In a recent study published in Scientific Reports, researchers showed that a simple string-pulling task could help make a reliable assessment of shoulder mobility across animals and humans. Across ...
Among patients with chronic noncancer pain, a novel machine learning model effectively predicts opioid use disorder risk.
For many diseases and chronic conditions, an individual's genes play a role in their likelihood of developing the disease. While some inherited diseases, such as cystic fibrosis or sickle cell anemia, ...
Background Early graft failure within 90 postoperative days is the leading cause of mortality after heart transplantation. Existing risk scores, based on linear regression, often struggle to capture ...
Cardiovascular disease continues to be the leading cause of death worldwide. To save lives, constantly improving diagnostic and risk assessments is vital. One researcher from the University of ...
Nursing homes (NHs) using the Preferences for Everyday Living Inventory (PELI-NH) to assess important preferences and provide person-centered care find the number of items to be a barrier to using the ...
Machine learning, a key enabler of artificial intelligence, is increasingly used for applications like self-driving cars, medical devices, and advanced robots that work near humans — all contexts ...
Schizophrenia is a severe and often highly debilitating psychiatric disorder characterized by distorted emotions, thinking patterns and altered perceptions of reality, as well as mental impairments.
In an interview with Technology Networks, Dr. Daniel Reker discusses how machine learning is improving data-scarce areas of drug discovery.
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