MIT professor’s quick primer on two types of machine learning for healthcare

There are two main approaches to machine learning – supervised and unsupervised – and each has specific applications in the context of healthcare. And even though their impact has not yet sent shockwaves through the industry, the potential of each is enormous, according to John Guttag, head of the Data Driven Inference Group at MIT’s Computer Science and Artificial Intelligence Laboratory. At its basic level, machine learning involves looking at data, and from that data finding information that is not readily visible. Example: Applying machine learning to data about patients infected with Zika or another virus and using what we can learn about what happens to those people to inform care decisions regarding the best ways to treat people who get infected in the future.  “Typically we use machine learning…


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