How big data can be used to improve early detection of cognitive disease — Cynthia Rudin

MIT Sloan Asst. Prof. Cynthia Rudin

MIT Sloan Asst. Prof. Cynthia Rudin

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.

Read the full post at The Health Care Blog.

Cynthia Rudin is an Associate Professor of Statistics at the MIT Sloan School of Management in Operations Research and Statistics.

New models to predict recidivism could provide better way to deter repeat crime — Cynthia Rudin

MIT Sloan Asst. Prof. Cynthia Rudin

MIT Sloan Asst. Prof. Cynthia Rudin

From The Conversation 

In the US, a minority of individuals commit the majority of crimes. In fact, about two-thirds of released prisoners are arrested again within three years of getting out of jail.

This begs the question: is there a way to predict which prisoners are more likely to become repeat offenders?

Recidivism prediction is important because it has significant applications in terms of allocating social services, policy-making, sentencing, probation and bail. From judges to social workers, all parties involved need to be able to work together and understand the risk posed by various individuals. Read More »

Using machine learning to increase meeting efficiency — Cynthia Rudin

MIT Sloan Asst. Prof. Cynthia Rudin

MIT Sloan Asst. Prof. Cynthia Rudin

Meetings play a big role in many people’s jobs. In the U.S. alone, an estimated 11 million meetings take place in a typical day. Managers can spend up to three-quarters of their time in meetings, and approximately 97% of workers say that collaboration is essential to do their best work.

As a result, meetings are tremendously important for businesses. Yet understanding meetings — much less finding ways to increase their productivity — is challenging for researchers because it requires an understanding of many social signals and complex interpersonal dynamics. Most of the work done in this area has been from the social sciences perspective using field work and surveys.

Read More »

Save Time on Search Engines: New Algorithm “Grows Lists”–Cynthia Rudin

MIT Sloan Asst. Prof. Cynthia Rudin

Have you ever tried to create a list of all upcoming events in your local area? If you live near Boston, it would be a useful list considering how bad traffic can be when there is a public event like a street festival or fundraising walk.

The problem is that although such events are planned well in advance, there is no online central list of events in Boston. Rather, there are many different sources of event listings, each of which is incomplete, such as Boston.com, Eventbrite and Yelp. Read More »