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Machine learning predicts potential complications in CT-guided thoracic biopsies

machine learning predicts potential complications in ct-guided thoracic biopsies

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CT-guided transthoracic biopsy performed by expert radiologists is both accurate and safe, and when paired with machine learning, can help physicians predict the likelihood of complications.Doctors from the University of Pennsylvania’s Perelman School of Medicine examined nearly 800 CTTB procedures performed in a tertiary hospital for their research published in Academic Radiology. In 97% of these minimally invasive procedures, diagnostic yield proved to be “excellent,” with complications occurring in only 2% of cases.

While CTTB is relatively safe and preferred over other methods to biopsy thoracic lesions, first author Eduardo J. Mortani Barbosa Jr., MD, and colleagues found a neural network model could predict the severity of complications when they do happen in up to 94% of situations.

“These results suggest that sophisticated statistical models, especially with machine learning, may be accurate enough to allow pre-procedure prediction of CTTB related complications, based solely on imaging and clinical data, therefore allowing preemptive preventive measures to be applied in higher risk patients,” the authors added.

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Artificial Intelligence at Core of Marine Officers’ ‘Big Ideas’ for Future of Force – USNI News

artificial intelligence at core of marine officers’ ‘big ideas’ for future of force – usni news

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A team of 10 Marines is mulling how to take major technology developments and apply them to the combat missions, as part of a Naval Postgraduate School-hosted series of online TED talk-styled presentations.
Artificial intelligence (AI), machine learning, virtual reality and other technological advances are at the center of the “Big Ideas Exchange.” The goal is moving the most promising idea from the theoretical to the practical as quickly as possible.
Several students said they drew inspiration for their thinking about the “future character of naval warfare” from Marine Corps Commandant Gen. David Berger’s 2019 guidance to the Marine Corps.
Citing the guidance, Col. Randolph Pugh, senior Marine Corps adviser at NPS in Monterey, Calif., said the students’ “Big Ideas” presentations are meant to not only stimulate thinking but also to facilitate moving the most promising ideas from thought to practice to doctrine.
Past exchanges have produced several ideas now being used. Capt. Courtney Thompson’s 2019 presentation on combat load affecting mission outcomes had an immediate impact in the 2nd Marine Division. Division Commanding General Maj. Gen. David Furness was quoted in the latest edition of Phalanx, a publication of national security analysis, saying they have applied the research to how “ …

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Can Artificial Intelligence Help Against Coronavirus?

can artificial intelligence help against coronavirus?

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Flattening the curve. Slowing the spread. I have yet to read — or hear — anyone say we are trying to stop the coronavirus virus. To beat it. The closest has been a news commentator saying that if everyone stopped in their tracks, six feet apart from everyone else, the spread of the virus would end immediately.
Of course, that is not going to happen. So we deal with realities. We try to predict who will get the virus; make diagnoses as quickly as possible; identify who will respond to therapy. In the absence of a well-proven treatment, we want to know who has the best chance of survival and should, therefore, get a ventilator.
These are not questions to be taken lightly. Amazingly, we don’t have the answers. Any of them. It is a stunning illustration of how little we know about the future of our species and the uncertain times in which we live — humbling hallmarks of a pandemic that fell on us suddenly.  
That artificial intelligence (AI) has become a beacon of hope should come as no surprise.
A Flurry of AI Reports
Since the outbreak began, there has been a flurry of reports published about how AI …

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AI System – Using Neural Networks With Deep Learning – Beats Stock Market in Simulation

ai system – using neural networks with deep learning – beats stock market in simulation

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Researchers in Italy have melded the emerging science of convolutional neural networks (CNNs) with deep learning — a discipline within artificial intelligence — to achieve a system of market forecasting with the potential for greater gains and fewer losses than previous attempts to use AI methods to manage stock portfolios. The team, led by Prof. Silvio Barra at the University of Cagliari, published their findings on IEEE/CAA Journal of Automatica Sinica.
The University of Cagliari-based team set out to create an AI-managed “buy and hold” (B&H) strategy — a system of deciding whether to take one of three possible actions — a long action (buying a stock and selling it before the market closes), a short action (selling a stock, then buying it back before the market closes), and a hold (deciding not to invest in a stock that day). At the heart of their proposed system is an automated cycle of analyzing layered images generated from current and past market data. Older B&H systems based their decisions on machine learning, a discipline that leans heavily on predictions based on past performance.
By letting their proposed network analyze current data layered over past data, they are taking market forecasting a step …

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Bluware Signs New Agreement with BP to Support Innovative Deep Learning Workflow in Subsurface Data Interpretation – EnterpriseTalk

bluware signs new agreement with bp to support innovative deep learning workflow in subsurface data interpretation – enterprisetalk

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Bluware Corp, the digital innovation platform that enables the oil and gas industry to accelerate digital transformation initiatives using deep learning, is pleased to announce a new agreement with BP (NYSE: BP). Bluware’s technology will help BP to improve quality and speed when delivering seismic interpretation products.
“BP recognizes the significant impact advances in digital technology can bring and we are pleased to implement Bluware InteractivAI™, a new and innovative deep learning technology, augmenting our geoscientists’ ability to accelerate subsurface data interpretation,” says Ahmed Hashmi, Upstream Chief Digital and Technology Officer at BP.
Large seismic data sets are difficult to move and use in workflows and time consuming to interpret. InteractivAI, powered by Bluware Volume Data Store (VDS™) cloud-native data environment, enables the acceleration of detailed interpretation tasks. With this tool geoscientists can now train and correct deep learning results interactively, significantly improving structural interpretation workflows.
“We are excited to be a part of BP’s digital innovation goals in delivering significant value and a better user experience across their subsurface workflows,” says Dan Piette, CEO of Bluware.

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Council Post: How Artificial Intelligence Can Lighten The Load For Customer Service Representatives

council post: how artificial intelligence can lighten the load for customer service representatives

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Over the last decade, experts have predicted that AI would make up the bulk of the current workforce. The World Economic Forum estimated that by 2025, humans would only make up 48% of the workforce, whereas digital workers would account for 52%. Although Accenture’s model showed that the risk of not adopting AI would be much greater than the risk of early adoption, it was based on an upbeat economic climate. With good returns on equity, companies have had little reason to galvanize radical change. Why fix something if it isn’t broken?
This has all changed, however. COVID-19 broke things. Economies stalled. Consumer spending dried up. Revenues tanked. Enterprises have started looking at new ways of doing things to simply survive the current situation. In this market, employing AI-powered digital colleagues doesn’t sound nearly as scary when enterprises need radical change to get out of this valley. This is particularly true when looking at deploying AI for customer service roles.
Customer Service Shortage
Even before this pandemic, enterprises were well versed in the difficulty of sustaining good customer service with seasonal variances in load. The demand for customer service has spikes, and the humans servicing calls can struggle to …

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The Machine Learning Fix For Healthcare Disbursement Delays

the machine learning fix for healthcare disbursement delays

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Posted on June 2, 2020 Financial institutions (FIs), businesses and healthcare providers are adjusting their operations to suit the new reality that the COVID-19 pandemic has caused. Healthcare provideers, for example, are turning to telehealth solutions to service patients for routine appointments as to keep those individuals from entering hospitals or doctors’ offices and risking exposure. Providers are also attempting to find ways to speed up the disbursements process to both doctors and to patients expecting funds from health claims. Innovating for speedy disbursements in the healthcare space can be tricky, because many of these payments come attached with reams of complicated paperwork with sensitive and personal medical information that must be verified before the money can be sent. Both consumers and physicians, however, are more frustrated by the longer wait times that come with check disbursements at a time when financial strains are more widespread.In the latest Disbursements Tracker®, PYMNTS examines how the COVID-19 pandemic is impacting the healthcare industry and how it is exposing issues with check disbursements. The Tracker also examines what digital technologies or payment methods may help to ease these frictions, as well as how the pandemic is continuing to affect government agencies and small businesses …

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All you need to know about symbolic artificial intelligence

all you need to know about symbolic artificial intelligence

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Image credit: DepositphotosThis article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI.
Today, artificial intelligence is mostly about artificial neural networks and deep learning. But this is not how it always was. In fact, for most of its six-decade history, the field was dominated by symbolic artificial intelligence, also known as “classical AI,” “rule-based AI,” and “good old-fashioned AI.”
Symbolic AI involves the explicit embedding of human knowledge and behavior rules into computer programs. The practice showed a lot of promise in the early decades of AI research. But in recent years, as neural networks, also known as connectionist AI, gained traction, symbolic AI has fallen by the wayside.
The role of symbols in artificial intelligence
Symbols are things we use to represent other things. Symbols play a vital role in the human thought and reasoning process. If I tell you that I saw a cat up in a tree, your mind will quickly conjure an image.
We use symbols all the time to define things (cat, car, airplane, etc.) and people (teacher, police, salesperson). Symbols can represent abstract concepts (bank transaction) or things that don’t physically exist ( …

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