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Byron Wallace, a machine learning expert and creator of RobotReviewer. (Credit: Adam Glanzman/Northeastern University)

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,” says 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. 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.

The ultimate goal is to expand RobotReviewer’s capabilities so the algorithm could analyze the entire evidence base and provide guidance for physicians at the bedside. While it won’t be quite like Amazon’s Alexa for doctors, the program could similarly provide useful information and recommendations on command.

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