Medicine’s Big Problem with Big Data: Information Hoarding

Researchers at IBM, Berg Pharma, Memorial Sloan Kettering, UC Berkeley and other institutions are exploring how artificial intelligence and big data can be used to develop better treatments for diseases.

But one of the biggest challenges for making full use of these computational tools in medicine is that vast amounts of data have been locked away — or never digitized in the first place.

The results of earlier research efforts or the experiences of individual patients are often trapped in the archives of pharmaceutical companies or the paper filing cabinets of doctors’ offices.

Patient privacy issues, competitive interests and the sheer lack of electronic records have prevented information sharing that could potentially reveal broader patterns in what appeared to any single doctor like an isolated incident.

When you can analyze clinical trials, genomic data and electronic medical records for 100,000 patients, “you see patterns that you don’t notice in a couple,” said Michael Keiser, an instructor at the UC San Francisco School of Medicine.

Given that promise, a number of organizations are beginning to pull together medical data sources.

Late last year, the American Society of Clinical Oncology announced the initial development of CancerLinQ, a “rapid learning system” that allows researchers to enter, access and analyze anonymized medical records of cancer patients.

Similarly, in April the CEO Roundtable on Cancer, a nonprofit representing major pharmaceutical companies, announced the launch of Project Data Sphere. It’s an open platform populated with clinical datasets from earlier Phase III studies conducted by AstraZeneca, Bayer, Celgene, Memorial Sloan Kettering, Pfizer, Sanofi and others.

The data has been harmonized and scrubbed of patient identifying details, enabling independent researchers or those working for life sciences companies to use it freely. They have access to built-in analytical tools, or can plug the data into their own software.


Patient privacy is important but so is making progress on cancer.

David Patterson, a professor of computer science at UC Berkeley developing machine learning tools for cancer research

It might uncover little known drug candidates that showed some effectiveness against certain mutations, but were basically abandoned when they didn’t directly attack the principle target of a particular study, said Dr. Martin Murphy, chief executive of the CEO Roundtable on Cancer.

In some cases, it could also eliminate the need for control groups — those who receive the standard of care plus a placebo instead of the experimental treatment — since earlier studies have already indicated the outcomes for those patients. (That would be an important development because the fear of receiving a placebo is a major reason many patients decide againstparticipating in clinical trials.)

The effort is happening now in part because of improving technology and in part because companies are coming around to the view that they’ll all be better off with the insights gleaned from this pooled data.

“It’s a recognition that it’s costing a lot more money to develop another drug,” Murphy said. “The low-hanging fruit was long ago harvested.”

Other information sharing efforts include the Global Alliance for Genomics and Health, the molecular databases maintained by EMBL-EBI and the National Institute of Health’s Biomarker Consortium.

Study: How Corporations are Deploying in the Collaborative Economy

Can big brands learn from Uber, Kickstarter, Airbnb and the Maker Movement? Yes, they’re using the same strategies to connect to their market, at a rapid pace.

The Collaborative Economy is a movement. People are empowered to fund, build, their own bespoke goods in the Maker Movement, and people are using new technologies to share what they already have in the Sharing Economy. In both cases, people are empowered to get what they need from each other –rather than buy from traditional companies.

Business models are changing, but corporations aren’t standing by idle, they’re quickly adapting and changing,you may access this detailed timeline. While we’ve yet to see return on investment numbers from any of these early deployments, our research of this same sample set indicate that thefrequency of brands deploying is increasing, even before the launch of Crowd Companies, six months ago.

A bit about the data and methods: Anongoing list of efforts has been collected from industry leaders, readers, and the brands themselves, we then tagged each of the deployments into specific categories. Most case examples are tagged in more than one instance, as they have overlapping deployments. Data was collected up until April 2014, and even more case examples are emerging. We identified a corporation as a company with characteristics of companies usually over 1000 employees or over a billion dollars gross revenue.

Three Graphs: Corporations in the Collaborative Economy

  1. Industry breakdown
  2. Major strategy
  3. Specific tactic(s)

Graph 1, above: Across the nearly 77 case examples up till April 2014, a majority of the deployments were from the retail industry, followed by the auto industry, then the technology space, then hospitality. In some cases, we counted consumer product goods and durable goods which impact the retail space in the frequency count.

Key findings:  Companies closely related to consumer type business models are most impacted –and therefore have done the most deployments.

Graph 2, above: Across the broad spectrum of the collaborative economy (maker movement, crowd funding, sharing economy, and more) corporations are deploying tactics that are related to the sharing economy. The one isolated event of a co-op, where the company is owned by the customers and employees, is REI. The second most common strategy was tapping the Maker Movement, often in the form of co-innovation, also known as outside-in innovation or other variations.

Key findings: Corporations gravitated towards sharing economy business models, often through sponsorships, partnerships with leading players like Uber –but this doesn’t guarantee business model resiliency beyond the media pickup.


Graph 3, above: This graph is a subset of the above “Strategy” breakdown, shown directly above. We found that specific tactics most companies are deploying “brands as a service”, which means products are sent on-demand or are available as rental business models –instead of ownership models. In particular, BMW, Peugeot, Daimler, rent cars directly to drivers, and hotels like Westin and Cosmo rent out workout gear and dresses on Rent The Runway, respectively.

Key findings: Brands deployed “Brand as a service” which often equates to a rental model, or on-demand model, to meet new market demands of “access over ownership”. Secondly, much of this was achieved through partnerships with players like Uber, or other on-demand players.  A few companies launched their own marketplaces, or partnered with other companies that offered this.

Conclusion: Brands must adopt Peer to Peer Commerce Models
Large corporations continue to adopt disruptive technologies. Twenty years ago, they adopted the internet, ten years ago, they adopted social media, and now, in 2014, they’re adopting the methods of the Collaborative Economy. The internet phase required an online B2C model, social media shifted to peer to peer communication, yet in this next phase, brands must offer their own peer to peer commerce models. In each of these phases, mindset changes are required, letting go of some control in order to gain more, and business model shifts. To learn more, find my body of work on the collaborative economywhich includes research, frameworks, graphics, data, and case examples.


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Learn How They Laid the Transatlantic Cable Back in 1959

Learn How They Laid the Transatlantic Cable Back in 1959SEXPAND

In this hyper-modern, satellite-powered wireless age, it’s easy to forget how not too long ago our main connection to Europe was a single cable: the TAT-1. This, the first submarine transatlantic telephone cable was finally completed in 1956, just in time for operators to realize they needed a second. Guess what they named that one.

TAT-2. They named it TAT-2, AT&T just posted a short film from its archives explaining in 1959 not only how the cables was laid but also how it worked. It’s a great watch for anybody who’s into history but especially for anyone who’s into infrastructure. The fiber optics cables that replaces the TAT-1 and TAT-2 in the late 1970s and early 1980s, respectively, still power the internet you’re reading this on. So without that early technology, the technology that’s now preserving its history might not exist. Combines Tons of Internet Troubleshooting Tools Into One Web Page Combines Tons of Internet Troubleshooting Tools Into One Web Page

When you are trying to troubleshoot internet connection issues, knowledge is power. consolidates 21 different tests on one handy page.

There’s a lot of good stuff here, from “Is this Site Down” to “Traceroute” and “IP Location Finder.” You can find all this information other places, of course, but this site puts it all onto one page. The one thing missing is your current IP address, but we’ve got an easy way to do that too. Check it out at the link below.

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Elon Musk: the new It Boy of Silicon Valley

When Hollywood wanted to bring to life Tony Stark, the comic-book engineering prodigy who grew up to be the billionaire industrialist and slick playboy alter ego of Iron Man, it turned to the closest thing the real world seemed to offer.

“We need to sit down with Elon Musk,” Robert Downey Jr, who plays Stark in the blockbuster film series, told his colleagues. Jon Favreau, the director, hailed Musk as “a Renaissance man in an era that needs them”.

He lacks – for now – the superhero’s jet-powered suit of armour. But in the 12 years since making his fortune from the sale ofPayPal, the online payments firm, Musk has become a man whose vast wealth, wild ambitions and turbulent personal life are scarcely less cartoonish. Next month, the buccaneering, South African-born entrepreneur, renowned for his pledge to colonise Mars one day, is expected to travel to London. He is due to hand over the keys to the first buyers of the British version of theModel S, the £70,000 electric car on which Tesla, Musk’s futuristic car manufacturer, is pinning its hopes.

The buzz surrounding its arrival has environmentalists longing for the sharp reduction in carbon emissions that could come from a long-awaited mainstream breakthrough in sales of electric cars. Only 1,547 were sold in Britain between January and April this year – 0.18% of the total. Having been eagerly snapped up by film stars, musicians and the doyens of Silicon Valley over the past two years, the Model S has become California’s third highest-selling luxury car. Its growing popularity followed a string of triumphs by SpaceX, another of Musk’s ventures, in its mission to dominate the commercial space exploration industry. Yet sceptics could be forgiven for wanting to kick the tyres of some of the 42-year-old tycoon’s more audacious claims.

From his days in Pretoria as a skinny kid nicknamed Muskrat, bullied by bigger boys, Musk was always different. He took refuge in his fantasy books, encyclopedias and computers. At the age of 12, he sold his first piece of software, a simple video game set in space called Blastar.

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The New Rules of Robot/Human Society

Peter Asaro, Kate Darling, and IEET Fellow Wendell Wallach talk about the morals and ethics that we will face very soon dealing with robotic technology.

As technology speeds forward, humans are beginning to imagine the day when robots will fill the roles promised to us in science fiction. But what should we be thinking about TODAY, as robots like military and delivery drones become a real part of our society? How should robots be programmed to interact with us? How should we treat robots? And who is responsible for a robot’s actions? As we look at the unexpected impact of new technologies, we are obligated as a society to consider the moral and ethical implications of robotics.

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Aliens Exist And Will Be Found Pretty Soon, Say Scientists

It used to be that if you asked an astronomer if there was intelligent life elsewhere in the universe, you’d get some sort of hedged response involving the vastness of the universe and statistical probabilities that you’d expect from a diligent scientist.

I’ve asked this question recently of a few astronomers from NASA, and also from the massive Keck observatory in Hawaii, and I’ve received a version of that same old response, but with a new preface that has become more common in recent years. It’s usually something like: “Well, we might be able to answer that question relatively soon.”

This past week, a few scientists took it a step further and gave the U.S. Congress a relative date by which they expect we’ll have discovered signs of intelligent life elsewhere in the universe.

“It is not hyperbolic to suggest that scientists could very well discover extraterrestrial intelligence within two decades’ time or less, given resources to conduct the search,” Seth Shostak, Senior Astronomer at the SETI Institute, said in testimony before the U.S. House Committee on Science, Space and Technology.

So there you have it. Aliens by 2034. That’s actually a few decades ahead of the date of first contact in the fictional “Star Trek” series — April 5, 2063.

It is worth noting that in the last decade, Shostak was floating the date 2025 as a likely end to our apparent cosmic isolation, and as recently as February he was talking about a date “two dozen years out.” So, clearly Shostak isn’t trying to win any bets by calling the specific date we find E.T., but rather the point is that the current rate of technological advancement makes it likely that we’ll be able to find that evidence within a single generation.

Much of the credit for this level of confidence among Astrobiologists like Shostak can be credited to discoveries made by the latest generation of telescopes, perhaps most notably the Kepler planet-hunting space telescope, which continues to deliver a steady stream of of revelations about just how common not only distant planets, but potential Earth analogs are in far-off solar systems.

“Recent analyses of Kepler data suggest that as many as one star in five will have a habitable, Earth-size planet in orbit around it,” Shostak told the lawmakers. “This number could be too large by perhaps a factor of two or three, but even so it implies that the Milky Way is home to 10 to 80 billion cousins of Earth.”

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Why the Moon Landings Could Have Never EVER Been Faked: The Definitive Proof

This video is so good, so incredibly brilliant, solid and simple, that you will want to paste it all over your Facebooks and Twitters just to piss off all the IMBECILES who still claim that the Moon landings were faked.* The reason is simple: the technology to fake it didn’t exist.

It’s a very simple argument. It’s not about showing how ignorant the hoaxers demonstrate to be with their idiotic “proofs”, which actually show they don’t know anything about physics, photography or even perspective. Or the fact that simple there’s tons of physical proof that we were there. Or the fact that the Soviet Union was monitoring it too and accepted the American victory in the Space Race.

No, it’s something even more obvious. This video explains why there was absolutely no way to fake it at the time. Even the cameras needed to fake it didn’t exist back then.

It’s completely convincing and undeniable argument and worth watching from beginning to end. I enjoyed it so much that I was giggling at some points. Especially one of them: we have gone from a world in which we couldn’t possibly fake a landing on the Moon but we went there for real to a world in which we are no longer going to the Moon but we can easily fake it.

Thinking & Writing : The CIA’s Guide to Cognitive Science & Intelligence Analysis

This CIA Monograph (re-released in 2010 by Robert Sinclair) presents “the implications of growing knowledge in the cognitive sciences for the way the intelligence business is conducted – in how we perform analysis, how we present our findings, and even its meaning for our hiring and training practices”. In other words, this paper is about, “thinking and writing [and] the complex mental patterns out of which writing comes, their strengths and limitations, and the challenges they create, not just for writers but for managers”. Below are some curated excerpts.

P.S. Don’t confuse this paper with the popular CIA book, “Psychology of Intelligence Analysis” which I have linked to in the past.  This paper draws upon similar cognitive research but has a different focus (mainly that of communicating clearly).

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