Biomed Engine

A blog about medical device science and technology

[biomedengine]

Kaggle is a platform for data prediction competitions. Companies, organizations and researchers post their data and have it scrutinized by the world's best statisticians.

Interested in free-lance data science?  Here is a place you might start! I stumbled upon this and found it really interesting.  Some tutorials and examples are available to get you started.

[ibmconsulting]

Algorithms Of The Mind: What Machine Learning Teaches Us About Ourselves Christopher Nguyen, huffingtonpost.com

“Science often follows technology, because inventions give us new ways to think about the world and new phenomena in need of explanation.”

Or so Aram Harrow, an MIT physics professor, counter-intuitively argues in “Why now is the right time to…

[biomedengine]

There is science behind technology adoption.  Here is a good place to start reading more, Crossing the Chasm: Marketing and Selling High-Tech Products to Mainstream Customers.  Sometimes I see words like “disruptive” cringe at the entrepreneur jargon, but I must admit it makes some sense and it is more relevant today than it was 25 years ago according to the author.

[biomedengine]

Biomimicry is the engineering and design of things based on nature. Here is an example of this principle in action to improve the design of surgical robots based on octopus bodies. Check it out for yourself! https://theconversation.com/how-we-made-an-octopus-inspired-surgical-robot-using-coffee-41852

[emergentfutures]

An NPR Reporter Raced A Machine To Write A News Story. Who Won?

“A company called Automated Insights created a program called WordSmith that generates simple news stories based on things like sporting events and financial news. The stories are published on Yahoo! and via the Associated Press, among other outlets.

We wanted to know: How would NPR’s best stack up against the machine?”

Full Story: NPR

[biomedengine]

More on machines vs. people. The idea that computers can take on more and more complex thought-like tasks appears to be at a turning point. Algorithms are not just affecting chess world champions, but also medicine, journalism and wrap lyrics as well. It's a blossoming field and with a little research you will find that the tools needed to start your own machine learning projects are within reach.

[emergentfutures]

Machine learning algorithms can ‘bust a rhyme’ better than humans by 21%

Researchers from Aalto University in Finland have been rapping with machine learning algorithms that can construct lyrical patterns in rap.

The machine rap artist, dubbed DeepBeat, uses algorithms, RankSVM and a deep neural network to achieve a rhyme density, based on the length and frequency of rhymes, that’s 21 per cent higher than the top ranked human rapper used in the experiment, Inspectah Deck.

Full Story: CIO

[biomedengine]

Very cool application... 8 mile vs 8 MHz

[biomedengine]

Get more out of your web research! I recently had a phone call with a product manager at a Dallas startup called Brainspace. They tout a very efficient machine learning algorithm that mines text in a far more complete way compared to a standard Google or yahoo search. In fact, they recommend performing searches on the content of an entire paragraph or article, because their engine will find the key word searches most relevant to the body of text. To boot, the interface is creative and user friendly. Check it out... www.brainspace.com

[biomedengine]

The MacGyver Cure for Cancer.

Great article on medical device development for solving a global health issue.  Their focus on practicality, teamwork, and drive make this story particularity interesting and satisfying.

[biomedengine]

Will it blend? Apparently a blender is just about all it takes to make industrial volumes of graphene!  Thanks to my enlightened friend I found out just how important this little idea could become.  Graphene may one day be used to generate batteries that charge in seconds, toxic waste sponges, bionic devices, more aerodynamic tennis rackets to be thrown farther in frustration, and of course as another reason for a Gizmodo post. 

http://gizmodo.com/how-you-can-make-graphene-at-home-in-your-blender-1565497683

http://gizmodo.com/5988977/9-incredible-uses-for-graphene

http://gizmodo.com/5985269/how-graphene-could-transform-the-gadgets-of-the-future

[biomedengine]

Machine learning and cancer killing.  Here is a brief look at how computers can aid clinical decision making in drug selection and dosing, which could lead to faster and more decisive cancer treatments. 

http://www.nytimes.com/2014/03/27/science/enlisting-a-computer-to-battle-cancers-one-by-one.html?_r=0

[biomedengine]

Biomed Engine turned 3 today!

[futurescope]

Google Glass proof of concept - Philips Healthcare

First healthcare wearable display proof of concept using Google Glass to send real time Philips Intellivue Solutions data.  Join the conversation on LinkedIn Innovations in Health:http://lnkd.in/bgTQbYi

[read more @Fast.Co]

[thinkvr]

33% of CEOs Worry They Are Out of Touch With Customers:

And that may be because they are seriously lagging in engaging customers in social business. “This entire chart should be flipped.”

Building a Better Customer Experience. - Business 2 Community

[biomedengine]

Samsung at ACL... Baller!

[biomedengine]

Electronic tattoo tracks the heat running through your veins

Inexpensive wearable sensors are a great way to provide a personalized touch to health care.  Something as convenient as this temperature sensor may be useful in detecting autonomic nervous system changes or even skin hydration according to the article in Nature Materials.

Thermometers don't seem like they need reinventing. Just stick one beneath your tongue, wait a minute, and everything you need to know is right in front of you. But there's a lot more that...

[biomedengine]

What gives them inspiration?

Here’s a collection of successful entrepreneurs’ thoughts on just that.  There are about 30 or so posts on this thread and here are some notable quotes.

“You can’t do it alone… …I knew that we could all learn better if we collaborated with each other. So I set out to change the system.”

- Giovanni Colella, MD, Co-Founder & CEO at Castlight Health

   “It is the personal tales of people I meet that are most inspiring… …Often I am more struck by the potential of an individual than their idea.”

- Richard Branson, Founder at Virgin Group

  “What the leader who gave me the assignment knew, and that I didn’t, is that people with leadership potential often don’t discover it and tap into it until they are faced with a truly difficult challenge, one that pulls them out of their comfort zone and makes failure a real option.”

- Steven J. Thompson, CEO at Johns Hopkins Medicine International

  And for good measure: “But for me, a consistent and reliable source of inspiration is Donald Trump.”

- Richard A. Moran, CEO, Corporate Director, Venture Capitalist, Author, Vintner

  Picture credit to the essay from Karl Heiselman, CEO at Wolff Olins.

[biomedengine]

Once I started thinking about decision making in the context of creating new ideas I found a few reasons to dig deeper.   For one, decision making is an essential part of the method that we use internally to iterate a fleeting thought into an idea that solves an important problem or an old problem in a better way.  Two, this process is often overlooked even in important business decisions.  Lastly, according to this blogger’s testimony we make as many as 35,000 decisions each day, which is reason enough in itself. 

With all these decisions happening I wondered, what constitutes a good decision maker from a bad one. I quickly found that this subject is way beyond the scope of a blog post, but I did find some useful information that puts a framework on a process we often perform in the background of our minds.  I broke the process down into four steps.

Step 1: realize the opportunity cost of decision making

The New York Times reported on an MIT study about decision making.  The test subjects (ultimately this could have been anyone on the web in 2008) were to choose between shapes and determine which was bigger.  The more correct answers you got the better.  Catch was that the size difference from question to question could be anywhere from was 2 – 25 %. The study found that poor performance was related to wasting time trying to differentiate shapes with only a 2% difference instead of just guessing and moving onto an easier comparison.  Crunching the numbers shows that participants spent, “64 percent of their time deciding between similar shapes, and only 36 percent of their time choosing between dissimilar shapes, Dr. Ariely reports.”  Almost everyone would have done better if they had weighted in the opportunity cost (the amount of time wasted on tough decisions vs. moving onto easier decisions).

The point is when it comes to decisions of equal importance, there is no benefit to spending more time on the difficult ones.  What color to paint the kitchen, yoga or spinning class, where to eat, how long to stay at our current job or get a new one?  They mean different things to different people, but are overall not worth a great deal of effort to solve.  So that puts an important stipulation on decision making in that you stand to lose out by not making it at the opportune time, or in missing out on other things while waffling.  This might be called dithering or decision paralysis, and I often hear it associated with the term fear of failure.

To get over decision paralysis sometimes simply picking a route and trying it out is best according to the “life’s an experiment approach.”  The knowledge gained, for better or worse, makes the next decision in that space more informed.

Step 2: weight your options & when stuck, experiment by choosing one of the options

Got it, but still how do I make better decisions?  There are about 357,107 amazon search results for the term ‘decision making’ leaving the finer details of this to an expert.  However I’ve been through this process recently and I found the tool we used to be very attractive (when time permits), which was a matrix.  We were stratifying the market opportunities for a medical device and had about eight medical problems the device could realistically attempt to solve.  The matrix consisted of the potential applications across the top and the methods of evaluating the application in the left column, such as cost of clinical trial, overcoming competitors, market size, fit with company IP, etc...  We could score and weight certain criteria by application and determine an overall ‘fit’ for the device in each market opportunity.  There are web resources available to follow this method and the graphical representation of the outcomes (e.g. a bubble chart) are often easy to interpret and make a stunning slide for a venture capital pitch.

In short, this step consists of deciding the options are, framing them in the context of your hierarchy, and applying the potential risk/benefits.  Be sure to check emotional and gut reactions, such as overemphasizing an option connected to a positive experience regardless of its impact on the project.  One of my favorite quotes from Einstein encompasses this, “a man should look for what is, and not for what he thinks should be.” 

Aside from emotions, decision fatigue can also be a negative contributor to the process.

Step 3: quantify or record the outcome

Next we record our results.  There seems to be a theme of scientific method in the last couple blog posts being that it applies to decision making as well.  Here we are using ourselves as the subject and choosing our own “treatment group.”  I think we’ve heard this time and again that sticking to the decision is critical here.  They say that will-power is strengthened, like a muscle, rather than inherent. 

Quantifying a decision's outcome objectively may not be possible, but attempting to do so could be a great benefit and exercise.  However, simply recording the experience in some way may suffice such as a picture or a blurb in a journal.

Step 4: when faced with future decisions, utilize past knowledge

When faced with the same or similar decision later, we are expected to get a more favorable outcome when past experience is considered.  Since we’ve recorded our single subject experiment there is a previous outcome to review and use to guide future decisions.  This is where objective, rather than subjective measurements are useful.

Step 5: fail as quickly and often as possible. 

I can’t understate the importance of this.  I first heard this in grad school in a number of ways from a number of people, and accepting this advice was difficult, but one of the best things that helped me in becoming a better researcher.  As far as start-up companies go I’ll point you to some of the many results on why and how to fail fast…

Forbes: http://www.forbes.com/sites/georgebradt/2013/01/23/want-to-fail-fast-do-these-three-things/

BusinessWeek: http://www.businessweek.com/stories/2007-06-24/fail-fast-fail-cheap

BusinessInsider: http://www.businessinsider.com/my-best-advice-to-entrepreneurs-fail-fast-fail-often-and-follow-your-passion-2011-8

500 Start-ups: http://500.co/2011/02/23/fail-fast-often-by-design/

Washington Post: http://www.washingtonpost.com/blogs/innovations/post/the-new-fail-fail-fast-fail-early-and-fail-often/2012/05/30/gJQAKA891U_blog.html