Current methods to measure glucose requires needles and repeated fingerpricks over the day. Fingerpricks can often be painful, deterring patient compliance. A new technology for detecting low glucose levels via ECG using a non-invasive wearable sensor, which with the latest Artificial Intelligence can detect hypoglycaemic events from raw ECG signals has been made by researchers from the University of Warwick. Currently Continuous Glucose Monitors (CGM) are available by the NHS for hypoglycaemia detection (sugar levels into blood or derma). They measure glucose in interstitial fluid using an invasive sensor with a little needle, which sends alarms and data to a display device.
Medtronic's mission is to alleviate pain, restore health, and extend life through the application of biomedical engineering, explains Elaine Gee, PhD, Senior Principal Algorithm Engineer specializing in Artificial Intelligence at Medtronic. It's a mission Gee is well equipped for. With over 15 years' experience in modeling, bioinformatics, and engineering, she drives machine learning algorithm development and analytics to support next-generation medical devices for diabetes management. On behalf of AI Trends, Ben Lakin, from Cambridge Innovation Institute, sat down with Gee to discuss her most recent focus: algorithm development related to glucose sensing to improve the accuracy and performance of continuous glucose monitoring devices, also known as CGMs. Editor's Note: Gee will be giving a featured presentation on Advancing Continuous Glucose Monitoring Sensor Development with Machine Learning at Sensors Summit in San Diego, December 10-12.
Sign in to report inappropriate content. A new technique developed by researchers at the University of Warwick uses the latest findings of Artificial Intelligence to detect hypoglycaemic events from raw ECG signals, via wearable sensors. The technology works with an 82% reliability, and could replace the need for invasive finger-prick testing with a needle, which could be particularly useful for paediatric age patients.
You are free to share this article under the Attribution 4.0 International license. A new technology for detecting low glucose levels uses artificial intelligence to detect hypoglycemic events with ECG signals from wearable sensors, researchers report. Tracking sugar in the blood is crucial for both healthy individuals and diabetic patients, but current methods to measure glucose require needles and repeated finger pricks throughout the day. Finger pricks can often be painful, deterring patient compliance. Currently Continuous Glucose Monitors (CGM) for hypoglycemia detection measure glucose in interstitial fluid using an invasive sensor with a little needle, which sends alarms and data to a display device.
Current methods to measure glucose requires needles and repeated fingerpicks over the day. Fingerpicks can often be painful, deterring patient compliance. A new technology for detecting low glucose levels via ECG using a non-invasive wearable sensor, which with the latest Artificial Intelligence can detect hypoglycaemic events from raw ECG signals has been made by researchers from the University of Warwick. Currently Continuous Glucose Monitors (CGM) are available by the NHS for hypoglycaemia detection (sugar levels into blood or derma). They measure glucose in interstitial fluid using an invasive sensor with a little needle, which sends alarms and data to a display device.
While technologies that impede, rather than enhance care, have made the healthcare industry somewhat skeptical of innovation, a shift toward patient-centric care is changing the game. Healthtech innovations in 2019 are helping to transform the business of care, creating efficiencies, cutting costs, and providing better outcomes. How these new technologies mesh with the clinical skill set of a medical provider is still being determined. Providers who embrace tools now available will help to determine how healthcare delivery looks in 2020 and well beyond. Here's what you need to know: If you aren't offering your patients virtual visits, it's likely they'll find someone who is Virtual visits, often conducted via a smartphone or personal computer, offer convenient access to care, saving patients the time and expense of traveling to an appointment and providing care to those who have limited access to it.
In a move that could help win over some skeptics about the value and efficacy of AI in clinical care, The American Diabetes Association, in its new set of clinical standards, recognizes the use of autonomous artificial intelligence for the screening of some medical conditions. WHY IT MATTERS The ADA's new 2020 Standards of Medical Care in Diabetes includes language noting that "AI systems that detect more than mild diabetic retinopathy and diabetic macular edema authorized for use by the FDA represent an alternative to traditional screening approaches." The clinical standards – published earlier this month in the peer-reviewed journal Diabetes Care – represent a new source for evidence-based best practices, consulted by hospitals and health systems, physicians, insurers and quality organizations. While acknowledging that autonomous AI can be an alternative to traditional screening, however, the ADA specifies that it feels the "benefits and optimal utilization of this type of screening have yet to be fully determined." In addition, it cautions that "artificial intelligence systems should not be used for patients with known retinopathy, prior retinopathy treatment, or symptoms of vision impairment."
The nation's leading association that fights against diabetes released a new set of clinical standards that for the first time include the use of autonomous artificial intelligence (AI). The American Diabetes Association (ADA)'s 2020 Standards of Medical Care in Diabetes states that, "AI systems that detect more than mild diabetic retinopathy and diabetic macular edema authorized for use by the FDA represent an alternative to traditional screening approaches." To date, IDx-DR is the first and only FDA-authorized autonomous AI diagnostic system for the detection of diabetic retinopathy and macular edema. It is currently in use at a number of large health systems that each serve tens of thousands of people with diabetes and have struggled to implement diabetic retinopathy eye exams at scale for their large diabetes population. "The ADA's inclusion of our technology in its Standards of Care marks a significant move toward mainstream adoption of autonomous AI in clinical care," said Michael Abramoff, MD, PhD, Founder and Executive Chairman at IDx. "Our early customers are visionary leaders who foresaw that autonomous AI would one day become a standard of care for diabetic retinopathy screening, and taking that leap is paying off for them. Already, health systems that are using IDx-DR have experienced significant improvements in accessibility, efficiency and compliance rates, unleashing massive potential for cost savings and improved patient outcomes."
Who wants to live forever? Until recently, the quest to slow ageing or even reverse it was the stuff of legends – or scams. But, today, an evidence-based race to delay or prevent ageing is energising scientists worldwide. Scientists say there are already a number of things we can do to extend life and health, while promising that current and ongoing large-scale trials of drugs and other interventions mean the once-mythical goal of healthy, longer-lived lives is not far away. "Death is inevitable but ageing is not," said Dr Nir Barzilai, founding director of the Institute for Aging Research at the Albert Einstein College of Medicine, New York.
--We introduce HT AD, a novel model for diagnosis prediction using Electronic Health Records (EHR) represented as Heterogeneous Information Networks. Recent studies on modeling EHR have shown success in automatically learning representations of the clinical records in order to avoid the need for manual feature selection. However, these representations are often learned and aggregated without specificity for the different possible targets being predicted. Our model introduces a target-aware hierarchical attention mechanism that allows it to learn to attend to the most important clinical records when aggregating their representations for prediction of a diagnosis. We evaluate our model using a publicly available benchmark dataset and demonstrate that the use of target-aware attention significantly improves performance compared to the current state of the art. Additionally, we propose a method for incorporating non-categorical data into our predictions and demonstrate that this technique leads to further performance improvements. Lastly, we demonstrate that the predictions made by our proposed model are easily interpretable. I NTRODUCTION Electronic Health Records (EHR) provide a comprehensive picture of patients' medical histories, consisting of information such as written clinician notes, medical imagery, prescriptions, and diagnoses.