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Mike Mastanduno October 10, 2017

A good data scientist will have command of a large breadth of knowledge, from machine learning and statistics to business instinct or software engineering. Part of what makes this job exciting is the possibility of driving insights or improvements from any one of those skills. A data scientist may or may not know all the skills ahead of time, but they are able to step back, understand where there might be a high return on investment, and learn the skills necessary to take advantage. Recently, our team announced the release…

Mike Levy October 02, 2017

It seems like every week we see another headline highlighting the promise of data to improve healthcare, from convolutional neural networks beating cardiologists at detecting cardiac arrhythmia to incredible advances in computer vision feeding speculation that radiologists will all soon be out of work. Given these developments, and the fact that machine learning now touches much of our day-to-day lives, you may wonder why aren’t all discussions with physicians informed by data-driven predictions about outcomes of care decisions? For example, suppose you’ve injured your knee skiing and…

Taylor Miller September 06, 2017

Goals of the Rewrite healthcareai is intended to serve a wide range of users, from the least technical to the most technical. As we worked with users from this entire spectrum, we found that there were some significant gaps and unnecessary pain points. We also took this opportunity to increase the quality and maintainability of the code. Paying down some of our technical debts will allow us (and already has) to add features more quickly, with less friction, and create a better experience for our team, our contributors in the…

Mike Levy August 28, 2017

Which is better: machine learning or statistics? Hopefully the way that question is phrased highlights its ridiculousness, but with all the hype around machine learning these days you’d be forgiven for thinking that machine learning is the answer to all data-related questions. However, statistics departments aren’t shuttering or transitioning wholesale to machine learning, and old-school statistical tests definitely still have a place in healthcare analytics. The two are highly related and share some underlying machinery, but they have different purposes, use cases, and caveats. In this post, we’ll discuss what…

Ethan Taft August 18, 2017

A few weeks ago, our blog featured a post about k-means clustering, an unsupervised machine learning method. We use unsupervised methods when we don’t have an explicit idea of what patterns exist in a dataset. Clustering can help us surface insights about groups that exist in the data that we may not know about. To separate data into clusters, k-means first needs to calculate the distance between each data point. That distance is used to help define the “similarity” between two points and is normally calculated using some continuous technique…

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