Data mining, text mining, natural language processing, and computational linguistics: some definitions

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Every once in a while an innocuous technical term suddenly enters public discourse with a bizarrely negative connotation. I first noticed the phenomenon some years ago, when I saw a Republican politician accusing Hillary Clinton of "parsing." From the disgust with which he said it, he clearly seemed to feel that parsing was morally equivalent to puppy-drowning. It seemed quite odd to me, since I'd only ever heard the word "parse" used to refer to the computer analysis of sentence structures. The most recent word to suddenly find itself stigmatized by Republicans (yes, it does somehow always seem to be Republican politicians who are involved in this particular kind of linguistic bullshittery) is "encryption."


Bad Data Science and Woody Allen

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For breakfast he requests wheat germ, organic honey and tiger's milk - food in 1973 thought to be healthy. The futuristic doctors reply: "You mean there was no deep fat? No steak or cream pies or... hot fudge?" and "Those were thought to be unhealthy... precisely the opposite of what we now know to be true." A recent article in the Wall Street Journal entitled "The Questionable Link Between Saturated Fat and Heart Disease" details scientific malpractice in research about what food is healthy or not. For over fifty years the scientific consensus was that fat - both saturated or not - is a "cause" of obesity, heart disease, and other chronic diseases.


Making big data small BizTimes Media Milwaukee

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We live in a world of health data. With fitness trackers, electronic health records, sleep monitoring and countless other ways to track and measure our health, we've entered an exciting era in which the flow of data to our doctors, pharmacists and other care providers is revolutionizing how and how fast health care services are delivered. It's also given consumers windows into their own health that was just a dream 20, 10, even five years ago. Not long ago, we really only got a picture of our health once a year when we went to our doctor for an annual check-up. We'd get blood drawn, blood pressure, weight and other vital statistics were taken, and our doctor would declare us healthy or give us things to work on.


Predicting Diabetes Using a Machine Learning Approach - DZone Big Data

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Diabetes is one of deadliest diseases in the world. It is not only a disease but also a creator of different kinds of diseases like heart attack, blindness, kidney diseases, etc. The normal identifying process is that patients need to visit a diagnostic center, consult their doctor, and sit tight for a day or more to get their reports. Moreover, every time they want to get their diagnosis report, they have to waste their money in vain. But with the rise of Machine Learning approaches we have the ability to find a solution to this issue, we have developed a system using data mining which has the ability to predict whether the patient has diabetes or not.


From big data, to AI-enabled services: the future of well-being and healthcare

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In the past few years, we observed the emergence of wearable technologies which have mostly been facilitated via fitness and well-being applications, although their true future potential lays in the disrupting force they are placing on the healthcare sector. As a matter of fact, nowadays, thanks to FitBit, Apple smart-watches and Nike connected shoes, we have the ability to track lots of information from our sleeping patterns to extensive body vitals. Nevertheless, the big question is: how do we use this information? To better understand the trends in healthcare and their application, a good example to examine is the work accomplished by Dutch company, Sensara. Sensara is a spin-off European research project: they offer a subscription service for the monitoring of silver consumers in their homes.