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Researchers at Northwell s Feinstein Institutes for Medical Research have developed an AI-powered predictive tool they say can assess patients for their risk of respiratory failure within 48 hours.
WHY IT MATTERS
New research, led by assistant professors Theodoros Zanos and Dr. Douglas Barnaby of the Feinstein Institutes, and published in the
Journal of Medical Internet Research, showed accuracy in identifying at-risk patients for earlier interventions such as critical care consultation and closer patient monitoring.
The project centered on electronic health record data from 11,525 patients who were admitted to 13 Northwell hospitals in spring of 2020, when the pandemic was peaking in the New York area. Of those patients, 933 (8%) were placed on ventilators within 48 hours of admission.
US-Based Gold Mining Firm Newcrest Rolls out a Software for its workforce that detects COVID19 Prior to Symptoms
The firm’s 8,000 employees are now able to use a mobile app to detect symptoms of sickness By IBTimes Staff Reporter Mother and Daughter Photo: Jenny Guerin
Newcrest Mining, one of the world’s largest gold mining companies has just announced the adoption of a new tech that provides early indicators of sickness, helping stop the spread of COVID19.
As a subsidiary of Newmont Mining, the world’s largest gold producer by number of ounces mined, protecting their employees’ safety isn’t just a smart business move, it’s a matter of public health for entire communities a responsibility they’re acutely aware of and readily accept.
Mount Sinai
Mount Sinai researchers have published one of the first studies using a machine learning technique called “federated learning” to examine electronic health records to better predict how COVID-19 patients will progress. The study was published in the Journal of Medical Internet Research – Medical Informatics on January 27.
The researchers said the emerging technique holds promise to create more robust machine learning models that extend beyond a single health system without compromising patient privacy. These models, in turn, can help triage patients and improve the quality of their care.
Federated learning is a technique that trains an algorithm across multiple devices or servers holding local data samples but avoids clinical data aggregation, which is undesirable for reasons including patient privacy issues. Mount Sinai researchers implemented and assessed federated learning models using data from electronic health records at five separate hospitals within the H
Empowering public health services with Microsoft
In the face of a global pandemic and increasingly demanding patient needs, Microsoft’s David Rhew shares how technology can deliver virtual care experiences and accelerate the distribution of a vaccine for Covid-19
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Between 2015 and 2050, the proportion of the world’s population over 60 years of age will nearly double from 12 per cent to 22 per cent, according to the World Health Organization.
“As we age, our ability to live independently, safely and confidently in our home diminishes,” says David Rhew, global chief medical officer and vice president of healthcare at Microsoft.
This change can occur gradually, over several years, due to progressive decline of physical and cognitive capabilities. Or it can be sudden and dramatic, such as after a stroke, heart attack or major fall. Either way, ageing in a safe and familiar location continues to be one of the most important goals for those affected and their careg
Machine Learning Technique Helps Predict COVID-19 Outcomes by Angela Mohan on January 28, 2021 at 3:10 PM
Journal of Medical Internet Research - Medical Informatics.
The researchers said the emerging technique holds promise to create more robust machine learning models that extend beyond a single health system without compromising patient privacy. These models, in turn, can help triage patients and improve the quality of their care.
Federated learning is a technique that trains an algorithm across multiple devices or servers holding local data samples but avoids clinical data aggregation, which is undesirable for reasons including patient privacy issues.
Mount Sinai researchers implemented and assessed federated learning models using data from electronic health records at five separate hospitals within the Health System to predict mortality in COVID-19 patients.