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23andMe s new COVID-19 resource. Consumer genomics company 23andMe has released a new interactive tool that allows users to see how various health factors could impact the risk of COVID-19 hospitalization. The COVID-19 Severity Calculator pulls data from the company s COVID-19 study, which included 10,000 participants diagnosed with the disease and 750 who were hospitalized.
23andMe warned that the tool shouldn t be used as a predictor for individual risk, and doesn t take lifestyle or certain underlying health conditions into account. However, it does identify certain characteristics that were frequently tied to severe symptoms and hospitalization, chief among which were obesity, Type 2 diabetes and lack of exercise.
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
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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
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. They compared the performa
Researchers have published one of the first studies using a Machine Learning (ML) technique called "federated learning" to examine electronic health records to better predict how Covid-19 patients will progress.The study, published in .