Machine learning was found to be superior to logistic risk scores in predicting intrahospital all-cause mortality after transcatheter aortic valve implantation (TAVI), according to study results published in Clinical Research in Cardiology. Current strategies for identifying patients eligible for TAVI rely on risk assessment tools such as the Society of Thoracic Surgeon's Risk Score (STS score). The predictive power of these tools is poor, and improved options for risk stratification of TAVI patients are needed. In this retrospective analysis of data from 451 patients, investigators aimed to evaluate whether machine learning models could be used to predict clinical outcomes for patients after TAVI. A total of 83 features, including patient demographics, comorbidities, laboratory data, electro- and echocardiogram findings, and computed tomography (CT) results, were used to train and test the predictive models.
Medtronic and Medicrea announced in a press release that they are in the process of finalizing the acquisition by the US company of the Lyon-based SME. The latter is one of the pioneers in transforming spinal surgery through artificial intelligence, predictive modeling and customized spinal implants. The agreement between the two players will be achieved through the acquisition by Medtronic of all outstanding Medicrea shares. With a focus on predictive medicine, Medicrea designs, manufactures and distributes more than 30 families of FDA-approved implantable devices, which have been used in more than 175,000 surgical procedures worldwide to date. Medicrea is a Lyon-based SME with 175 employees, 35 of whom work in its subsidiary Medicrea USA Corp. based in New York. The company has its own ultra-modern production unit in Lyon, dedicated to the machining and development of custom implants by 3D printing from titanium powder.
Two people looking at the exact same scene before them may perceive it differently as a result of a so-called'fingerprint of misperception'. Researchers at the University of California Berkeley found natural variation in the inherent visual ability to pinpoint the exact location and size of objects. A series of experiments on nine individuals found'dramatic differences' in the ability to resolve fine details as well as discrepancies in judging location and size. The differences are due to how the brain processes visual stimuli, the academics believe, but the exact neural network responsible for the variation remains unknown. 'We assume our perception is a perfect reflection of the physical world around us, but this study shows that each of us has a unique visual fingerprint,' study lead author Miss Zixuan Wang, a UC Berkeley doctoral student in psychology, told Berkeley News.
I consult and educate companies to transform technology and data into a valuable, measurable, and monetizable business asset. In my data analytics and machine learning (ML) consulting engagements, I often come across use cases aimed at solving scientific problems using data, such as predicting the failure of a turbine or forecasting the carbon footprint of our IT data center. But what exactly is a scientific problem, and how is it different from a data problem? Is it really necessary to validate a known scientific fact or model again with data? Before answering these questions, let's define some key terms and scientific laws needed to answer these questions.
The COVID-19 pandemic is an incredibly complex and rapidly evolving global public health emergency. Facebook is committed to preventing the spread of false and misleading information on our platforms. Misinformation about the disease can evolve as rapidly as the headlines in the news and can be hard to distinguish from legitimate reporting. The same piece of misinformation can appear in slightly different forms, such as as an image modified with a few pixels cropped or augmented with a filter. And these variations can be unintentional or the result of someone's deliberate attempt to avoid detection.
Although the initial wave of the SARS-CoV-2 pandemic has abated in many countries, healthcare providers are still looking to identify as many COVID-19 patients as possible and contain the disease. Fast and accurate diagnosis is especially important when unsuspecting patients with a coronavirus infection come to the hospital with health complaints but don't yet show symptoms of COVID-19. Nasal swab samples analyzed by RT-PCR are currently recommended for the diagnosis of COVID-19, however, supply shortages, a wait time of up to two days for results, and a false negative rate as high as 1 in 5 mean alternative, large-scale COVID-19 screening tools are still being sought. SARS-CoV-2 is known to damage lung tissue, and in a distinct way that doctors are now seeking to exploit for new diagnostic approaches. Many COVID-19 patients develop pneumonia, which can progress to respiratory failure and sometimes death.
Until recently, the application of artificial intelligence (AI) in healthcare was a source of much speculation but little action. However, since IBM began attempting to develop healthcare applications for its "Watson" AI in 2015 (Lohr 2015; Strickland 2019), uses of AI in medicine have become tangible in a range of fields. While surveys of the industry fail to yield a single definition of AI, it is generally considered to refer to "mathematical algorithms processed by computers" that "have the ability to learn" (Zwieg, Tran and Evans 2018). Defining AI as "a set of technologies that allow machines and computers to simulate human intelligence" (Wang and Preininger 2019), clinical researchers frequently compare AI to human performance as a means of validation. Results favoring the algorithms in fields such as dermatology and radiology have provoked anxiety about job displacement in the clinical specialties that cognitive machines are expected to replace (Budd 2019).
At our AI-focused Transform 2020 event, taking place July 15-17 entirely online, VentureBeat will recognize and award emergent, compelling, and influential work through our second annual VB AI Innovation Awards. Drawn from our daily editorial coverage and the expertise of our nominating committee members, these awards give us a chance to shine a light on the people and companies making an impact in AI. Here are the nominees in each of the five categories -- NLP/NLU Innovation, Business Application Innovation, Computer Vision Innovation, AI for Good, and Startup Spotlight. A senior principal scientist at Amazon Research and faculty member at the University of California, Santa Cruz, Dr. Hakkani-Tur currently works on solving natural dialogue for Amazon's Alexa AI. She has researched and worked on natural language processing, conversational AI, and more for over two decades, including stints at Google and Microsoft.
Major tech stocks drove the markets lower this morning, with Nasdaq NDAQ down by almost 0.5%. In contrast, the Dow was trading higher by 200 points buoyed by banking stocks like JP Morgan and Citigroup C, which have beaten street estimates on earnings this morning. Of course, by mid-morning, the Nasdaq had turned positive. More choppiness should be expected as more companies declare their quarterly results throughout the week. Our deep learning algorithms have gone through the data and used Artificial Intelligence ("AI") to help you spot the Top Buys for today.