Is It Time to Hang Out Your Own Shingle?
April 23, 2019

Being your own boss is the dream of many people, but few actually take the plunge. With today’s economic climate and technological advances, it might be the best time to consider “hanging out your own shingle”. Some people take the plunge after a layoff, while others see an opportunity and seize it. Independent workers (freelancers, contractors or business owners) are on the rise according to the Harvard Business Review. This group is expected to rise from about 17 million in 2013 to 23 million by 2017. Many people ask if it is the right time - and the truth is, there isn’t a perfect time to start your own company but here are some considerations: Do you crave the idea of running your own business? – Most people who start their own company have thought about working for themselves for a while. They have an idea they have wanted to develop or feel that they could be more focused and bring a specific skill to an industry they already have experience in. Regardless of the reason, people who start their own business have a certain tolerance for risk and are willing to deal with the uncertainties of working for themselves to see their idea become a reality. Laura was a Marketing Manager for a large corporation...

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Machine Learning – why we don’t want to trust
April 23, 2019

Old ways of thinking are hard to let go. Growth often means moving past our old ways of thinking but sometimes we are reluctant to hand over control to machines. It is much easier to get behind leaders who trust their gut instincts than to trust that machines are capable of seeing complex relationships that we don’t and can simulate outcomes that we never can. This year has allowed me to start trusting machines a bit.

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Big Data - How healthcare is slowly adopting
April 23, 2019

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How Data Science Will Power the Future
April 23, 2019

Big data is transforming industries at a rapid rate, and the age of big data is here to stay. With more and more data being collected than ever before, the ability to extract meaning from data is only going to grow in demand. The power of data is already shaping our lives in ways that we probably don’t realize and with so much of our lives leaving an electronic footprint more insights are being developed about our personal habits.

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Data – if I knew then what I know now
April 23, 2019

Some of the worst management decisions in business history have been made at companies with huge market share. It is difficult to ruin a company with a single decision but not all decisions are created equal. Kodak and Motorola management missed huge shifts in their industries until it was too late. Kodak held the patent for digital cameras long before they were massed produced but made the decision not to invest in the technology for fear it would undercut sales of its film business. Motorola held a 22% market share in mobile phones in 2006 with the success of their stylish Razr cellphone but they failed to launch a new generation of smartphones. By the time the company released a new line of Razr phones in 2010, Motorola had to compete with products such as the BlackBerry and iPhone. The Apple iPhone entered the market in 2007 with a 17% market share while Motorola saw its stock price plummet.

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Cancer – Your Data May Save You
April 23, 2019

In recent work, WPC Healthcare used an approach to diagnosing omentum cancer that yielded a 93.4% area–under-the-curve using only 12 biomarkers of the over 50,000 provided, creating a 32.9% improvement. In an era of big data, being able to use smarter modeling approaches may lead to faster predictions and better accuracy. We believe the WPC Healthcare approach could significantly improve detection for cancers in the areas of breast, colon, endometrium, kidney, lung, ovary, prostrate, and uterus.

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Diabetics Have New Super Power in Fight Against Retinopathy - Big Data
April 23, 2019

WPC Healthcare implements a “better together” approach by using retinal images with data science to provide early warning of disease progression for diabetic retinopathy. EyePACS, a provider of picture archive communication systems, offered the original data set for analysis in order to improve accuracy of the stage of diabetic retinopathy. Using the industry standard, a five-part scoring system, each individual patient was evaluated for degree of risk, and the results compared with the physicians original diagnosis. Most ophthalmologist see about 5,000 patients per year (or 10,000 eyes).

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Five Critical Lessons You Should Learn from the IBM Watson Misfire
April 23, 2019

Forbes recently published an article about the $62M Misfire MD Anderson had in its project to use IBM Watson designed to integrate the knowledge of MD Anderson’s clinicians and researchers to treat patients with the most effective, safe and evidence-based standard of care available. Creation of machine learning models is a small part of the overall process in performing data analysis or data science. In the zeal to incorporate AI into healthcare and other industries, several lessons stand out to me that we have learned when working with clients who want to leverage data science.

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My Dad Died of What?
April 23, 2019

Your Dad is being taken by ambulance to the ER and you need to call the hospital now. I heard those dreaded words several years ago. By the time I located the attending ER Physician who had worked with him, he was in the ICU on life support. All of this unfolded in a matter of minutes. He lived out-of-state and I had just spoken with him two days prior - he seemed fine. The ER doctor shared that he had been speaking with the EMTs but was unresponsive when he arrived in the ER. I received his death certificate a week later and cause of death was heart failure. He had the heart of someone years younger so I called the hospital back and located the ER Physician who initially treated him. He remembered my Dad and explained to me that he died of sepsis.

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Can Machine Learning Make Grand Rounds “Grand” Again?
April 23, 2019

Grand rounds are not so grand, but can machine learning help make them grand again? Most physicians learn the art of diagnosis in medical school but the art of diagnosing a disease is currently not a straightforward process. With all the reports about machine learning replacing physicians, it is unlikely physicians will be replaced but can technology help doctors be better? The concept of grand rounds can be used within machine learning to recognize the very best diagnoses and treatment patterns that can be applied to new patients.

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