Enhancing ambulance response using MLNew research at the Victoria University of Wellington seeks to apply machine learning models to improve emergency response times.
Backed by a grant from New Zealand’s Ministry of Business, Innovation and…
Healthcare organizations have no shortage of data about their patients, not only from electronic health records (EHRs) but also from remote patient monitoring. So far, however, it has been difficult for IT and clinical…
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Adonis, a healthcare revenue cycle intelligence and automation company, announced it secured $31 million in Series B funding, bringing its total raise to $54 million. Point72 Private Investments led the round with participation from new…
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The current prostate cancer (PCa) screen test, prostate-specific antigen (PSA), has a high sensitivity for PCa but low specificity for high-risk, clinically significant PCa (csPCa), resulting in overdiagnosis and overtreatment of non-csPCa. Early identification…
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The Medicines and Healthcare products Regulatory Agency (MHRA) has published guiding principles for machine learning medical devices focusing on transparency.The guidance on the transparency of machine learning medical devices, published on 13 June 2024,…
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What You Should Know:
– Vultr, the world’s largest, privately-held cloud computing platform, today announced that Athos Therapeutics, Inc. (“Athos”), a clinical-stage biotechnology company, has chosen Vultr Cloud GPU to run its AI model training,…
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Update applications with modern technologies previously unavailable to legacy systems. These may include containers for deploying a single application in multiple environments, machine learning and artificial intelligence (AI) for mining large data stores, and…
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Ming Tang, the leader at the forefront of the NHS’ data revolution in England, will make a welcome return as national keynote at the Digital Health AI and Data conference 2024.The event, dedicated to…
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By Shania Kennedy
June 13, 2024 – Researchers from Washington University in St. Louis have developed a machine learning (ML) approach to predict recovery outcomes following lumbar spine surgery using data from wearables and other sources.
The…
By Shania Kennedy
June 11, 2024 – A Duke University-led research team has developed a machine learning (ML) tool designed to assist clinicians in accurately reading the electroencephalography (EEG) charts of patients in intensive care.
EEGs are…
