Artificial intelligence, big data and harnessing the “body's hidden drugs” MedCity News Berg, a Boston-area startup, builds off that concept of studying healthy tissues to understand the body's molecular and cellular natural defenses – and what...
Big data holds broad-ranging opportunities for many industries, helping analysts focus on important information that can translate to meaningful applications and discoveries. The health care field is a prime example of this potential where a number of big data-driven efforts are currently underway that could ultimately deliver an array of advances ranging from more precise patient diagnoses to new discoveries that provide enhanced treatment for patients living with chronic conditions. A Wall Street Journal article on the new startup Enlitic notes how the company is leveraging big data via a new software program using photographic identification and algorithms to facilitate sharper medical diagnoses. In addition, Wired reports that this technology not only has the potential to help detect diseases, but could also “make new discoveries by uncovering previously unnoticed patterns in the data.” Big data analytics is also aiding disease research in an unprecedented way. ComputerWeekly documents how a new research project combining wearable technologies, big data analytics and public cloud computing could yield important new discoveries for Parkinson’s disease. The effort, spearheaded by the Michael J. Fox Foundation in collaboration with Intel, is monitoring thousands of patients who are participating in the program by wearing smartwatches that gather and transmit each individual’s data moment-to-moment, in real time. Using the big data analytics platform on the Amazon Web Services cloud, researchers expect to cull precise information that sheds light on how Parkinson’s progresses and could help lead to new breakthroughs in treatment. Click headline to read more--
This article is by Peter Daboll, CEO of Ace Metrix. Big data is about integrating data and analyzing patterns. Big data is not concerned with collecting the data – that’s because in today’s internet of things, data is abundant.
matthew kapp's insight:
There is some real wisdom here. As example the abundance of information available for medical research and health is still not embracing the spectrum of information that is available. It's all so transformative that just the information domain of reverse correlating DNA and groups of medical records to discover gene, disease and drug treatments has overwhelmed the attention boundary of researchers. Add the spectrum of social, geospatial, environmental, nutrition and life mapping to name a few more relevant health data research domains would provide significantly richer information beyond just our inherited disposition to disease but also our environment. It will provide markers to know more than just what our DNA suggests but what our life style and environments suggests. With real world data from sites like Patientslikeme, perhaps we will learn what drugs and other treatments do and don't work for us also bringing in and ruling out alternative treatments that may work, do nothing or even be harming us. We would be better equipped then to prevent disease let alone getting more precision cures. Add to that transparency of the asymmetrical doctors fees and insurer policy within a health care system and nations will cut billions of dollars off the health bill from overpriced or unnecessary and immoral prescriptions.
Artificial Intelligence (A.I.) is having a moment, albeit one marked by crucial ambiguities. Cognoscenti including Stephen Hawking, Elon Musk and Bill Gates, among others, have recently weighed in on its potential and perils.
The Health eHeart Study attracts patients using connected medical devices like the AliveCor Heart Monitor to remotely capture cardiology data. The goal is to use big data to create cohorts, identify trends, and ultimately solve some of the biggest challenges in heart health.
Baidu's Surprising Search For The Holy Grail Of Artificial Intelligence Forbes A lot of the things that we care about are image tagging and object detection–classic computer vision tasks that are very valuable to things like image search and...
Greylock Partners’ Jerry Chen shares the triangular frameworks he uses to analyze opportunities and spot disruptive technologies in the IT infrastructure and enterprise application spaces.
matthew kapp's insight:
This is not a big data article but interesting description of the convergence happening in enterprise infrastructure and the disruptive new companies that are driving it. With 10 fold improvements in price performance this is set to be a significant disruptive pattern.
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