The Penn Medicine Institute for Biomedical Informatics announced the launch of a free, open-source automated machine learning system designed to simplify data analysis.
WHY IT MATTERS
Dubbed Penn AI, the artificial intelligence engine behind the platform can work out different analyses with different variables and methods on its own, and by making Penn AI’s analysis open source, it allows researchers to see the mechanisms behind each analysis.
As Penn AI is used more and more, it will continually learn the best methods for analyzing data and will provide recommendations for its users based on what they are looking to find out.
ON THE RECORD
“The problem with machine learning tools is that machine learning people build them, so they’re usually only usable by those with high levels of training,” Jason Moore, director of the Institute for Biomedical Informatics, said in a statement.
The three-year development period of the system was built in such a way as to make approachable by anyone, regardless of training or experience.
Moore explained the development team’s goal was to make a free and simple system that was still robust enough to transform the way the industry approaches biomedical research.
The aim of Penn AI is to be self-service, clinical platform, for instance making it possible for a doctor to query associations between sex, age, smoking and different diseases, and then have the platform answer their questions.
“I think this is really going to accelerate biomedical research,” Moore noted. “We’ll be able to do almost instantly what it takes weeks and months—and thousands or millions of dollars—to do now.”
Future versions of the platform could include more complex features for advanced users, like the addition of “ensemble approaches”, a technique that allows multiple machine learning apparatuses to work on the same dataset at the same time in order to develop a more robust analysis.
THE BIGGER TREND
The use of AI and machine learning is broadening as health systems and universities continue to explore applications ranging from patient safety to biomedicine.
A team of researchers from the Massachusetts Institute of Technology (MIT) recently found a deep learning artificial intelligence platform had a high success rate in detecting breast cancer risk.
Meanwhile, health application developer Clarigent Health is teaming up with The Children’s Home of Cincinnati to complete a pilot study using Clarigent’s artificial intelligence-powered mobile decision support app.
In an effort to help hospitals better aggregate data from disparate electronic health records after mergers, Wolters Kluwer announced in April that it had developed technology using machine learning to improve the process mapping lab results and other data to standardized LOINC codes.
Nathan Eddy is a healthcare and technology freelancer based in Berlin.
Email the writer: [email protected]
Healthcare IT News is a HIMSS Media publication.
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