Penn Medicine Releases Open Source, 'Self-Service' Artificial Intelligence Tool for Data Analytics

"Penn AI" is now accessible to anyone from high school students to biomedical researchers, on any computer or laptop

PHILADELPHIA - May 16, 2019 - The Penn Medicine Institute for Biomedical Informatics has launched a free, open-source automated machine learning system for data analysis that is designed for anyone to use, from a high school student looking to gain insight on their baseball team's statistics, to trained researchers looking for associations between cancer and environmental factors. "Penn AI," the first widely available tool of its kind, seeks to lower the barrier for entry into artificial intelligence, allowing users to bring in their own datasets or use the several hundred that are available for download within the tool. With a user-friendly dashboard easily run on a laptop, Penn AI is also designed to learn as it goes, ultimately making analysis suggestions based on the "experience" it gains through use.

"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," said Jason Moore, PhD, director of the Institute for Biomedical Informatics and a professor of Biostatistics, Epidemiology and Informatics. "My team has taken three years to develop this system so that it can be approachable by anyone, regardless of their training or experience. Our goal has been to make a free and simple system that is still robust enough to transform the way we approach biomedical research-which I think we've accomplished."

Penn AI is an automated machine learning system, which means that the artificial intelligence engine behind it can work out different analyses with different variables and methods on its own, without needing human input. Machine learning without automation requires someone to choose a specific method and manually adjust each parameter that the AI engine works on, often requiring more advanced knowledge of data in order to get meaningful results. Even for people with that know-how, there is still some guesswork involved. However, automation can eliminate much of that, and 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 glean.

By removing some of the complexities and adding the element of automation, Moore and his team believe that they can also make it much more common in clinical spaces.

"I want this to be self-service, clinical AI," Moore said. "I believe that this tool can make it so that it will soon be routine for a doctor to say, 'I want to look at the associations between sex, age, smoking and different diseases,' and then have this tool answer their questions."

Additionally, by making Penn AI's analysis open source, it allows doctors to see the mechanisms behind each analysis-how the tool got to each endpoint. Other programs available are expensive, don't do all of the things that Penn AI can and, most importantly, don't allow a look inside their coding.

"If you're going to use machine learning for patients, you want to trust it completely," Moore said. "You want to be able to look under the hood. This allows for that, which builds some faith among clinicians, which is important for user buy-in."

In Moore's words, Penn Medicine is a "data rich environment," but that may not be the case for every health system. As such, Moore hopes that Penn AI will provide an outlet for researchers in any locations to dig deeper into what information they have collected and to share it-of course, in a de-identified way.

"I think this is really going to accelerate biomedical research," Moore said. "We'll be able to do almost instantly what it takes weeks and months-and thousands or millions of dollars-to do now."

Moving forward, Moore envisions adding more complex features that more advanced users could utilize. For example, he'd like to add in 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 development of this tool was supported by National Institutes of Health research grants (R01 LM010098 and R01 AI116794) and National Institutes of Health infrastructure and support grants (UC4 DK112217, P30 ES013508, and UL1 TR001878).

Members of the development team included Heather Williams, Steve Vitale, Sharon Tartarone, Weixuan Fu, William La Cava, Josh Cohen, Randal Olson, Patryk Orzechowski, John Holmes, Moshe Sipper, and Ryan Urbanowicz.

Penn Medicine is one of the world's leading academic medical centers, dedicated to the related missions of medical education, biomedical research, and excellence in patient care. Penn Medicine consists of the Raymond and Ruth Perelman School of Medicine at the University of Pennsylvania (founded in 1765 as the nation's first medical school) and the University of Pennsylvania Health System, which together form a $7.8 billion enterprise.

The Perelman School of Medicine has been ranked among the top medical schools in the United States for more than 20 years, according to U.S. News & World Report's survey of research-oriented medical schools. The School is consistently among the nation's top recipients of funding from the National Institutes of Health, with $425 million awarded in the 2018 fiscal year.

The University of Pennsylvania Health System's patient care facilities include: the Hospital of the University of Pennsylvania and Penn Presbyterian Medical Center-which are recognized as one of the nation's top "Honor Roll" hospitals by U.S. News & World Report-Chester County Hospital; Lancaster General Health; Penn Medicine Princeton Health; and Pennsylvania Hospital, the nation's first hospital, founded in 1751. Additional facilities and enterprises include Good Shepherd Penn Partners, Penn Home Care and Hospice Services, Lancaster Behavioral Health Hospital, and Princeton House Behavioral Health, among others.

Penn Medicine is powered by a talented and dedicated workforce of more than 40,000 people. The organization also has alliances with top community health systems across both Southeastern Pennsylvania and Southern New Jersey, creating more options for patients no matter where they live.

Penn Medicine is committed to improving lives and health through a variety of community-based programs and activities. In fiscal year 2018, Penn Medicine provided more than $525 million to benefit our community.

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