Both approaches identified hemoglobin as one of the most significant predictors of CKD risk. Additional top-ranked features included blood urea, sodium levels, red blood cell count, potassium, and ...

MIT researchers have identified significant examples of machine-learning model failure when those models are applied to data other than what they were trained on, raising questions about the need to ...

Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered ...

Machine learning enhances proteomics by optimizing peptide identification, structure prediction, and biomarker discovery.

Yale researchers have developed a machine learning model, called Immunostruct, that can help scientists create more ...

The severity of symptoms in posttraumatic stress disorder (PTSD) varies greatly across individuals in the first year after trauma and it remains difficult to predict whether someone might worsen, ...

Machine learning models are usually complimented for their intelligence. However, their success mostly hinges on one fundamental aspect: data labeling for machine learning. A model has to get familiar ...

A powerful artificial intelligence (AI) tool could give clinicians a head start in identifying life-threatening complications ...

Forbes contributors publish independent expert analyses and insights. Writes about the future of finance and technology, follow for more. We live in a world where machines can understand speech, ...

Machine learning can predict many things, but can it predict who will develop schizophrenia years before the average ...