Abstract: Diabetes and its complications, especially Diabetic Retinopathy (DR) and Diabetic Nephropathy (DN) is a big challenge to the global healthcare system and needs accurate predictive models to ...

Abstract: machine learning (ML) with 5G technology has revolutionized smart healthcare. It has helped improve the quality of care, such as real-time analysis, decision-making, patient monitoring, and ...

Purpose The depth and breadth of clinical data within electronic health record (EHR) systems paired with innovative machine learning methods can be leveraged to identify novel risk factors for complex ...

Researchers have developed an AI-guided, shape-changing microneedle patch that speeds wound healing while reducing infection ...

17 ). Based on this, the present study intends to use a retrospective cohort design to construct and validate a risk prediction model for EFI in sepsis patients using ML algorithms. The goal is to ...

Machine learning approach i.e., support vector machine supported by leave-one-out cross-validation was used to build and test the classifier. We identified a 9-gene panel (IFI27, ITGB5, CTSD, EFNA4, ...

Osteoporotic fractures are a major health challenge in older adults. Despite the availability of safe and effective therapies for osteoporosis, these therapies are underused in individuals at high ...

The box from the Finnish company MedicubeX is now being used in various care scenarios. In an interview with heise online, founder and CEO Vili Kostamo discusses the company's latest projects, ...

More than 24,000 BGE customers remained without power Monday morning after back-to-back weekend storms.

A 22-year-old Stanford grad raised $11.6M to build a wearable that infers hormone levels from your wrist. Can inference ...

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