Catching the Gorilla: Applying Machine Learning to Electronic Health Records
September 29, 2017
September 29, 2017
By Ori Geva
In this special guest feature, Ori Geva, Co-Founder and CEO of Medial EarlySign, discusses how the use of machine learning can help create new opportunities for earlier intervention and delivery of improved, personalized care by allowing healthcare systems increase their scope of attention. Medial EarlySign is a developer of machine learning tools for data driven medicine. The company’s advanced algorithm platform accurately detects the likelihood of disease for subpopulations using basic medical information, such as blood test results, and other EMR data. Predictive tools provide physicians with actionable insight, while providing insurers with effective models to flag and focus on patients at risk, helping to prioritize resources, save money and improve care. Medial EarlySign’s platform addresses numerous potential clinical outcomes, including cancers, diabetes and other life-threatening illnesses.
Many of us are familiar with the Invisible Gorilla Experiment, the world-famous awareness test conducted by Daniel Simons and Christopher Chabris. If you haven’t seen it, here’s a quick refresher: Two groups of people wearing black and white T-shirts pass a basketball and study subjects are asked to count the number of passes made by one of the teams. In the middle of the drill, a man in a gorilla costume walks into the picture.
Surprisingly, more than 50% of the test subjects completely miss the gorilla. They were so engaged in the task of counting the number of passes that they simply didn’t see him. This experiment, originally conducted in 1999, still enthralls people to this day, shedding light on the psychological process of selective attention and inattentional blindness.
Read the full article here.