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Tag: Machine learning models

Ayasdi–Big Data changing hospital operations

One of the more interesting companies playing in the analytics space is Ayasdi. We’ve featured them at Health 2.0 a couple of times, but at HIMSS I got a chance to talk a little more in depth with chief medical officer Francis Campion about exactly how they parse apart huge numbers of data points, usually from EMRs, and then operationalize changes for their clients. The end result is more effective care and lower variability across different facilities, for example changing when drugs are delivered before surgery in order to improve outcomes. And increasingly their clients are doing this over multiple clinical pathways. They’re really on the cutting edge of how data will change care delivery (a tenet of our definition of Health 2.0) so watch the interview to hear and see more!

How Big Data Can Be Used to Improve Early Detection of Cognitive Disease

ClockThe aging of populations worldwide is leading to many healthcare challenges, such as an increase in dementia patients. One recent estimate suggests that 13.9% of people above age 70 currently suffer from some form of dementia like Alzheimer’s or dementia associated with Parkinson’s disease. The Alzheimer’s Association predicts that by 2050, 135 million people globally will suffer from Alzheimer’s disease.

While these are daunting numbers, some forms of cognitive diseases can be slowed if caught early enough. The key is early detection. In a recent study, my colleague and I found that machine learning can offer significantly better tools for early detection than what is traditionally used by physicians.

One of the more common traditional methods for screening and diagnosing cognitive decline is called the Clock Drawing Test. Used for over 50 years, this well-accepted tool asks subjects to draw a clock on a blank sheet of paper showing a specified time. Then they are asked to copy a pre-drawn clock showing that time. This paper and pencil test is quick and easy to administer, noninvasive, and inexpensive. However, the results are based on the subjective judgment of clinicians who score the tests. For instance, doctors must determine whether the clock circle has “only minor distortion” and whether the hour hand is “clearly shorter” than the minute hand.

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