Each team was working from a data set comprised of de-identified information on over 130,000 ICU stays which had been manipulated to exacerbate existing racial and ethnic biases. The groups were to investigate the effect of a faulty pulse oximeter reading, the effect of a missing blood lactate level and the effect of the combination of the two on mortality prediction in the hospital. Their challenge was to create a model that predicted mortality — while taking into account how the biases would influence the prediction model.

Jorge Reyes, a 10th grader at the Met School who is interested in a career in business, was familiar with data sets from the Data Science, AI and You in Health Care course, which he’d taken at school. However, the data set in the challenge went into much greater detail, he said, and was impressively comprehensive.

“It’s interesting to see how much data you can put in to create new graphs,” Reyes said.

Other students echoed that sentiment: East Greenwich High School ninth graders Mukti Patel and Mathew Claeson were both interested in seeing what they could do with their challenge, and how they could explore the different variables.

“I like the act of digging into the data, and I’m also interested in learning more about the power of AI and how it helps with coding,” Claeson said. “Through this event, I’m hoping to gain more knowledge about AI.”

Reyes, who said he’d been looking forward to meeting professional mentors at the event, had been assigned to the same table as Gabrielle Masse, a regulatory coordinator at the Lifespan Cancer Institute. Masse pointed out to her group that their data set included information about patients on ventilators. The group decided to include this factor in their prediction model — the idea being that patients on ventilators may have higher mortality risks.

The group’s ventilator graph was evidence that the datathon was working, said Dr. Sandeep Jain, a hematology/oncology fellow with the Warren Alpert Medical School who is affiliated with the Brown/Lifespan Center for Clinical Cancer Informatics and Data Science.

“This team is looking at different aspects of the data that we, as organizers, didn’t even think about,” Jain said. “They’re making discoveries on the spot. I got so excited when I heard them talking about that!”

This was the second year of the Health AI Systems Thinking for Equity Datathon; last year’s event took place at MIT. But commuting to Cambridge in rush hour traffic had proven challenging for the Rhode Island students. Brown was more centrally located and offered willing volunteers from the Warren Alpert Medical School and its computer science graduate programs, who joined mentors from Brown, MIT and other universities around the world.

“I really want to celebrate that Jeremy, Hamish and Sandeep, as well as all the participating Brown mentors, stepped in and saved the day for this program by holding the datathon in Providence, which is much more convenient for the students,” Eller said.

The high school students’ participation was supported and praised by Rhode Island leaders, including Angélica Infante-Green, Rhode Island commissioner of elementary and secondary education, who spoke on the first morning of the datathon, and U.S. Congressman Gabe Amo, who delivered closing remarks on day two.

The experience was well worth the short trip from South Providence, said Doug Rademacher, who teaches the data science and AI in health care course at the Met School.

“I really value my students seeing professionals they can relate to, and people from a variety of disciplines working together to solve difficult problems,” Rademacher said. “I also think it’s great for them to hear a doctor or a professor saying ‘I don’t know’ and encouraging the student to share their own ideas.”