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
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