Health Care: Data an Issue

June 28, 2018

Healthcare analytics is helping doctors and patients make decisions in ways we never could have dreamed. From helping keep your heart healthy to deciding when to have major surgery, analytic numbers make a big impact. However, that data needs to be perfect in order to work, according a recent ZD Net story, “Google AI is Very Good at Predicting When a Patient is Going to Die.”

According to the story:

“As noted, 80 percent of the effort in creating an analytic model is in cleaning the data, so it could provide a way to scale up predictive models, assuming the data is available to mine…. “This technique would allow clinicians to check whether a prediction is based on credible facts and address concerns about so-called ‘black-box’ methods that don’t explain why a prediction has been made.”

This really illustrates how powerful clean data can be in the health field. However, cleaning data is just about the most misunderstood wallflower in the often tedious world of machine learning and data science—not just in healthcare. According to Entrepreneur magazine, the act of filling in blanks, removing outliers, and basically looking at all the data to make sure it will be accurate, is the most important part of the process and also the hardest role to fill on a team.

Garbage in, garbage out. True decades ago. True today. How do we know? Just ask one of IBM Watson’s former health care specialists. Querying patients who were on the wrong end of a smart output may be helpful as well.

Patrick Roland, June 28, 2018


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