The medical field has a data problem. The issue isn’t a lack of data, but rather a lack of structure. Every year researchers publish thousands of studies describing the results of clinical trials. But there is no easy way to sift through them all.

“I believe that medicine should be data-driven,” said Byron Wallace, assistant professor in the College of Computer and Information Science at Northeastern. “If it’s not based on what the evidence says, treatment decisions are based on folklore.”

Wallace, a machine learning expert, is developing a tool called RobotReviewer that seeks to make mountains of data from research studies more accessible to healthcare providers. It uses machine learning algorithms and natural language processing models to automatically crawl through and make sense of scientific literature.

RobotReviewer is funded by a grant from the National Institutes of Health’s Big Data to Knowledge program. Currently, the platform can analyze a few articles at a time, assessing the robustness of the findings and providing some clinically relevant information.

For example, the RobotReviewer can detect whether a study included randomized control trials. The algorithm also checks for blinding—a study design feature in which the leading researcher doesn’t know which group of participants received the treatment being tested and which received the placebo.

Blinding and randomized control trials are two of the most important measures of scientific rigor for clinical studies. If RobotReviewer determines a study doesn’t meet these criteria, physicians may not want to take those findings into account as they recommend treatment for their patients.

Wallace is working with Iain Marshall, a general practitioner based in London, to develop the software.

“Iain finds himself struggling when he’s practicing because if a patient comes in with a particular condition, it’s really hard for him to say, ‘Here’s what the evidence actually says for your case,’” Wallace said. That’s because of the sheer amount of evidence available, and the lack of structure in the data, which makes it impossible to efficiently comb through.

The way RobotReviewer analyzes published studies is similar to the way an email client distinguishes spam from the rest of your messages.

“To decide whether an email should go to your spam filter, the software basically discovers correlations between words and frequency of emails being spam,” Wallace explains. The email client might be programmed to link the words, “flash sale” or “limited time only” to an instance of spam.

RobotReviewer might link the words “double-blind” or “randomized” to salient information about clinical studies. But to do that, a person first must label those words so the algorithm can learn to detect them.