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One-stop machine learning platform turns health care data into insights

Over the past decade, hospitals and other health care providers have put massive amounts of time and energy into adopting electronic health care records, turning hastily scribbled doctors' notes into durable sources of information. But collecting these data is less than half the battle. It can take even more time and effort to turn these records into actual insights — ones that use the learnings of the past to inform future decisions.

Cardea, a software system built by researchers and software engineers at MIT's Data to AI Lab (DAI Lab), is built to help with that. By shepherding hospital data through an ever-increasing set of machine learning models, the system could assist hospitals in planning for events as large as global pandemics and as small as no-show appointments.

With Cardea, hospitals may eventually be able to solve "hundreds of different types of machine learning problems," says Kalyan Veeramanchaneni, principal investigator of the DAI Lab and a principal research scientist in MIT's Laboratory for Information and Decision Systems (LIDS). Because the framework is open-source, and uses generalizable techniques, they can also share these solutions with each other, increasing transparency and enabling teamwork.

Automated for the people

Cardea belongs to a field called automated machine learning, or AutoML. Machine learning is increasingly common, used for everything from drug development to credit card fraud detection. The goal of AutoML is to democratize these predictive tools, making it easier for people — including, eventually, non-experts — to build, use, and understand them, says Veeramachaneni.

Instead of requiring people to design and code an entire machine learning model, AutoML systems like Cardea surface existing ones, along with explanations of what they do and how they work. Users can then mix and match modules to accomplish their goals, like going to a buffet rather than cooking a meal from scratch.

For instance, data scientists have built a number of machine learning tools for health care, but most of them aren't very accessible — even to experts. "They're written up in papers and hidden away," says Sarah Alnegheimish, a graduate student in LIDS. To build Cardea, she and her colleagues have been unearthing these tools and bringing them together, aiming to form "a powerful reference" for hospital problem-solvers, she says.

Step by step

To turn reams of data into useful predictions, Cardea walks users through a pipeline, with choices and safeguards at each step. They are first greeted by a data assembler, which ingests the information they provide. Cardea is built to work with Fast Healthcare Interoperability Resources (FHIR), the current industry standard for electronic health care records.

Hospitals vary in exactly how


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