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AI of the needle: Here’s how neural networks could detect nighttime low blood-sugar levels using your heart beat



Academics have applied for a patent describing how a neural network can detect low blood-sugar levels by analyzing heartbeat patterns rather than a blood sample.

Keeping track of glucose levels is annoying and painful. Multiple times per day, diabetics have to prick their finger, place the small drop of blood on a test strip, and insert that strip into a glucometer to get a reading, and then dial up their insulin dosage, or eat or drink carbohydrates, as necessary. In the US, at least, these strips aren’t cheap, thanks to the healthcare system.

The AI-based method developed by the team, however, is non-invasive. It only requires people to wear a device that can measure electrocardiograms (ECG), recordings of heartbeats made by sensors placed on the skin. Abnormal blood glucose levels can affect ECG readings; high levels of sugar lead to rapid heart rates, whereas low levels correlate to low heart rates.

The ECGs are then processed by a convolutional neural network (CNN) and a recurrent neural network (RNN) to flag up episodes of nocturnal hypoglycemia, a condition where glucose levels below a normal range during sleep.

Described as a “pilot study,” the researchers recruited four volunteers to wear devices that measure both ECGs and a non-invasive continuous glucose monitor (CGM). Over the course of up to 14 days, they studied each person’s pulse at times when their heart rates were normal and when they were affected by nocturnal hypoglycemic events. The data from the ECG and CGM were correlated and used to train the CNN and RNN to predict when blood glucose levels dip below normal levels from an individual’s heart rate.

Some of the ECG readings were held back for testing, and the results showed the team’s neural networks were on average accurate roughly 82 per cent time.

“Our approach enables personalized tuning of detection algorithms and emphasizes how hypoglycaemic events affect ECG in individuals,” said Leandro Pecchia, co-author of the paper and an associate professor of biomedical engineering at the University of Warwick, England. “Based on this information, clinicians can adapt the therapy to each individual.”


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But before any diabetics out there get their hopes up over such a device, the team admitted their patent has to go through much more clinical testing. Firstly, not only is their research sample size small, but none of the participants had type 1 or type 2 diabetes.

“Our study concerned the detection of nocturnal non-induced low glucose levels in healthy individuals; several clinical studies showed that cardiac changes could have different intensities in healthy, type 1 and type 2 diabetic persons,” the paper said.

So far, the results do show that applying deep learning on ECG can detect low blood glucose events and that training on personalized data makes it more effective for individuals. The goal is to eventually develop a device for diabetics that alerts them whenever their glucose levels dip to dangerous levels in their sleep.

The team applied to patent their technology “Electrocardiogra-based blood glucose level monitoring” in the UK in August last year, and described it in a paper published in Nature on Monday. ®

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Two Sigma Ventures raises $288M, complementing its $60B hedge fund parent




Eight years ago, Two Sigma Investments began an experiment in early-stage investing.

The hedge fund, focused on data-driven quantitative investing, was well on its way to amassing the $60 billion in assets under management that it currently holds, but wanted more exposure to early-stage technology companies, so it created a venture capital arm, Two Sigma Ventures.

At the time of the firm’s launch it made a series of investments, totaling about $70 million, exclusively with internal capital. The second fund was a $150 million vehicle that was backed primarily by the hedge fund, but included a few external limited partners.

Now, eight years and several investments later, the firm has raised $288 million in new funding from outside investors and is pushing to prove out its model, which leverages its parent company’s network of 1,700 data scientists, engineers and industry experts to support development inside its portfolio.

The world is becoming awash in data and there’s continuing advances in the science of computing,” says Two Sigma Ventures co-founder Colin Beirne. “We thought eight years ago when when started, that more and more companies of the future would be tapping into those trends.”

Beirne describes the firm’s investment thesis as being centered on backing data-driven companies across any sector — from consumer technology companies like the social networking monitoring application, Bark, or the high-performance, high-end sports wearable company, Whoop.

Alongside Beirne, Two Sigma Ventures is led by three other partners: Dan Abelon, who co-founded SpeedDate and sold it to IAC; Lindsey Gray, who launched and led NYU’s Entrepreneurial Institute; and Villi Iltchev, a former general partner at August Capital.

Recent investments in the firm’s portfolio include Firedome, an endpoint security company; NewtonX, which provides a database of experts; Radar, a location-based data analysis company; and Terray Therapeutics, which uses machine learning for drug discovery.

Other companies in the firm’s portfolio are farther afield. These include the New York-based Amper Music, which uses machine learning to make new music; and Zymergen, which uses machine learning and big data to identify genetic variations useful in pharmaceutical and industrial manufacturing.

Currently, the firm’s portfolio is divided between enterprise investments, consumer-facing deals and healthcare-focused technologies. The biggest bucket is enterprise software companies, which Beirne estimates represents about 65% of the portfolio. He expects the firm to become more active in healthcare investments going forward.

“We really think that the intersection of data and biology is going to change how healthcare is delivered,” Beirne says. “That looks dramatically different a decade from now.”

To seed the market for investments, the firm’s partners have also backed the Allen Institute’s investment fund for artificial intelligence startups.

Together with Sequoia, KPCB and Madrona, Two Sigma recently invested in a $10 million financing to seed companies that are working with AI. “This is a strategic investment from partner capital,” says Beirne.

Typically startups can expect Two Sigma to invest between $5 million and $10 million with its initial commitment. The firm will commit up to roughly $15 million in its portfolio companies over time.

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German football league Bundesliga teams with AWS to improve fan experience




Germany’s top soccer (football) league, Bundesliga, announced today it is partnering with AWS to use artificial intelligence to enhance the fan experience during games.

Andreas Heyden, executive vice president for digital sports at the Deutsche Fußball Liga, the entity that runs Bundesliga, says that this could take many forms, depending on whether the fan is watching a broadcast of the game or interacting online.

“We try to use technology in a way to excite a fan more, to engage a fan more, to really take the fan experience to the next level, to show relevant stats at the relevant time through broadcasting, in apps and on the web to personalize the customer experience,” Heyden said.

This could involve delivering personalized content. “In times like this when attention spans are shrinking, when a user opens up the app the first message should be the most relevant message in that context in that time for the specific user,” he said.

It also can help provide advanced statistics to fans in real time, even going so far as to predict the probability of a goal being scored at any particular moment in a game that would have an impact on your team. Heyden thinks of it as telling a story with numbers, rather than reporting what happened after the fact.

“We want to, with the help of technology, tell stories that could not have been told without the technology. There’s no chance that a reporter could come up with a number of what the probability of a shot [scoring in a given moment]. AWS can,” he said.

Werner Vogels, CTO at Amazon, says this about using machine learning and other technologies on the AWS platform to add to the experience of watching the game, which should help attract younger fans, regardless of the sport. “All of these kind of augmented customer fan experiences are crucial in engaging a whole new generation of fans,” Vogels told TechCrunch.

He adds that this kind of experience simply wasn’t possible until recently because the technology didn’t exist. “These things were impossible five or 10 years ago, mostly because now with all the machine learning software, as well as how the [pace of technology] has accelerated at such a [rate] at AWS, we’re now able to do these things in real time for sports fans.”

Bundesliga is not just any football league. It is the second biggest in the world in terms of revenue, and boasts the highest stadium attendance of all football teams worldwide. Today’s announcement is an extension of an ongoing relationship between DFL and AWS, which started in 2015 when Heyden helped move the league’s operations to the cloud on AWS.

Heyden says that it’s not a coincidence he ended up using AWS instead of another cloud company. He has known Vogels (who also happens to be a huge soccer fan) for many years, and has been using AWS for more than a decade, even well before he joined the DFL. Today’s announcement is an extension of that long-term relationship.

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Delta Air Lines startup partnerships are fueling innovation




For the first time, this year Delta Air Lines had a large presence at CES. The carrier used much of its space to highlight the “parallel reality” screens developed by Misapplied Sciences and Sarcos Robotics, which brought its latest Guardian exoskeleton. At the show, I sat down with COO Gil West, an industry veteran with years of experience at a number of airlines and airplane manufacturers, to talk about how the company works with these startups.

Like all large companies, Delta has gone through a bit of a digital transformation in recent years by rebuilding a lot of the technical infrastructure that powers its internal and external services (though like all airlines, it also still has plenty of legacy tech that is hard to replace). This work enabled the company to move faster, rethink a lot of its processes and heightened the reality that a lot of this innovation has to come from outside the company.

“If you think about where we are as a world right now, it’s a Renaissance period for transportation,” West said. “Now, fortunately, we’re right in the middle of it, but if you think about the different modes of transportation and autonomous and electrification — and the technologies like AI and ML — everything is converging. There’s truly, I think, a transportation revolution — and we’ll play in it.

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