Machine Learning Leading to Revolution in Clinical Data Management

Original article can be found here (source): Artificial Intelligence on Medium

Machine Learning Leading to Revolution in Clinical Data Management

“What I’ve been looking at for the past few years is how things are evolving within the clinical trial space, and what impact that’s going to have on clinical data management,” said Francis Kendall, Senior Director of Biostatistics and Programming at Cytel, explained to attendees in Orlando during the Summit for Clinical Ops Executives (SCOPE). We’re going to see a shift in how clinical evidence is produced and where it’s produced from, said Kendall. “It’s a new paradigm about data usage,” he said “We have traditional clinical trials, and they will always remain, but we’re starting to see things like pragmatic trials or synthetically controlled models. How do we deal with that data?” Data standards have embraced this paradigm shift, Kendall says. Efforts to provide standards for data, including FHIR, which looks at the interoperability of data, and the FAIR data principles are examples of the industry’s response to approaching standardized data, but we’re still playing around with how to structure data as a life sciences industry. “In a way, we’re probably one of the only industries that doesn’t come to grips with standardizing our data,” Kendall said. We need to look at this in a different way, says Kendall. “Rather than try to shoehorn data to the standard, we need to look at the data and see how we can gain value.” The data itself is changing as well. “We have more types of patient data as we go forward. It’s not just the traditional data; there’s a lot we can get, from omics data to tracking data.” Researchers want to be able to pull insights from that data, but how? Machine learning is one solution that has been gaining traction in other industries outside of healthcare, says Kendall. “There are some good examples in finance for instance where the traditional data manager or data administrator — who would create a program and look at the data [themselves] — now just manage how the algorithms are running as theymanage the data,” he said.

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