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Deploying and evaluating a machine learning intervention on clinical care and patient outcomes is a key step in moving clinical deterioration models from b

Deploying and evaluating a machine learning intervention to improve clinical care and patient outcomes is a key step in moving clinical deterioration model

Deploying and evaluating a machine learning intervention to improve clinical care and patient outcomes is a key step in moving clinical deterioration models from byte to bedside, according to a June 13 editorial in Critical Care Medicine that comments on a Mount Sinai study published in the same issue.

Researchers at the Icahn School of Medicine at Mount Sinai have made a significant breakthrough in Alzheimer's disease research by identifying a novel way to potentially slow down or even halt disease progression. The study, which focuses on the role of reactive astrocytes and the plexin-B1 protein in Alzheimer's pathophysiology, provides crucial insights into brain cell communication and opens the door to innovative treatment strategies. It was published in Nature Neuroscience on May 27.

Working under the umbrella of the PsychENCODE Consortium, the mental health research project established in 2015 by the National Institutes of Health, a team of Mount Sinai scientists has uncovered important new insights into the molecular biology of neuropsychiatric disease through two new studies published in a special issue of Science on Friday, May 24.