From The Health Care Blog
The 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.
In our study, we created an improved version of this test using big data and machine learning. For the past seven years, a group of neuropsychologists have had patients use a digital pen to draw the clocks instead of a pencil, accumulating more than 3,400 tests in that time. The pen functions as an ordinary ballpoint, but it also records its position on the page with considerable spatial and temporal accuracy. We applied machine learning algorithms to this body of data, constructing a data-driven diagnostic tool. So rather than having doctors subjectively analyze the pencil-drawn clocks, the data from the digital pen drawings goes into the machine learning algorithm’s model which provides the result of the test.
Cynthia Rudin is an Associate Professor of Statistics at the MIT Sloan School of Management in Operations Research and Statistics.