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Stroke is no longer just a danger for seniors as more young adults are suffering them. Lieutenant Governor John Fetterman is 52. Now, Main Line Health has a new technology to help stop strokes faster, if they're recognized quickly. Dominique Jones from Wynnefield just didn't feel right one morning in March. "When I was taking my shower, I had a headache," she recalled.
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New and continuously improving treatment options such as thrombolysis and thrombectomy have revolutionized acute stroke treatment in recent years. Following modern rhythms, the next revolution might well be the strategic use of the steadily increasing amounts of patient-related data for generating models enabling individualized outcome predictions. Milestones have already been achieved in several health care domains, as big data and artificial intelligence have entered everyday life. The aim of this review is to synoptically illustrate and discuss how artificial intelligence approaches may help to compute single-patient predictions in stroke outcome research in the acute, subacute and chronic stage. We will present approaches considering demographic, clinical and electrophysiological data, as well as data originating from various imaging modalities and combinations thereof. We will outline their advantages, disadvantages, their potential pitfalls and the promises they hold with a special focus on a clinical audience.
Much work has been done within artificial intelligence (AI) to better understand how the human brain works to make AI more efficient. When you use a search on Google or Amazon, or you type on a phone or tablet, a piece of technology known as predictive language model is being used. This AI-driven functionality is what allows technology to be able to predict the next word within a string of text. The most recent generation of these predictive language models also learns the underlying meaning of languages, such as question answering or story completion. Could these AI next-word prediction models provide insight into how the brain processes language? This is what a team of cognitive neuroscientists from MIT wanted to explore.
Machine learning is playing an ever-increasing role in biomedical research. Scientists at the Technical University of Munich (TUM) have now developed a new method of using molecular data to extract subtypes of illnesses. In the future, this method can help to support the study of larger patient groups. Nowadays doctors define and diagnose most diseases on the basis of symptoms. However, that does not necessarily mean that the illnesses of patients with similar symptoms will have identical causes or demonstrate the same molecular changes.
The bleak and all-too-common spectacle of roadkill was upsetting to Vedant Srinivas -- particularly when his uncle and cousin's beloved German Shepherd-Rottweiler mix was fatally hit by a car. More importantly, the losses made the high school student wonder if he could do something about it. What if Srinivas could stop the pet owners' broken hearts, save wildlife and deflect the economic impacts caused by the collisions? This month his efforts were rewarded. The sophomore from Eastlake High School in Sammamish, Wash., brought home a $5,000, first place grand award for the category of Environmental Engineering from the Regeneron International Science and Engineering Fair (ISEF).
An app which uses cognitive behavioural therapy techniques to help people overcome insomnia has received recommendation from the National Institute for Health and Care Excellence (NICE). Sleepio, from Big Health, uses an artificial intelligence (AI) algorithm to provide people with tailored therapy and provides a digital six-week self-help programme involving a sleep test, weekly interactive sessions with users encouraged to keep a diary about their sleeping patterns. Sleepio was rolled out in the south of England towards the end of 2018 and in 2019 was made available across London. NICE is recommending that the Sleepio app is used as cost-effective alternative to prescribed medication after is Medical Technologies Advisory Committee evaluated the platform. The committee concluded that Sleepio is more effective than conventional treatment options (sleep hygiene and medication) in reducing symptoms of insomnia in adults.
Investigators have identified characteristics of individuals with long COVID and those who are likely to have it by using machine learning techniques. The investigators, who were supported by the National Institutes of Health (NHI), analyzed a collection of electronic health records (EHR) available for COVID-19 research to help better identify who has long COVID. Investigators used the EHR data, from the National COVID Cohort Collaborative (N3C), a centralized national public database led by the NIH's National Centers for Advancing Translation Sciences, to identify more than 100,000 likely cases of long COVID, as of October 2021 and 200,000 cases as of May 2022. "It made sense to take advantage of modern data analysis tools and a unique big data resource like N3C, where many features of long COVID can be represented," Emily Pfaff, PhD, a clinical informaticist at the University of North Carolina at Chapel Hill, said in a statement. The N3C data includes information representing more than 13 million individuals nationwide and nearly 5 million positive COVID-19 cases.