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Levi Thatcher April 23, 2018

­­­­We started healthcare.ai in late 2016 to bring machine learning (ML) to the healthcare masses. As we release version 2.0 of the software (on April 20th), it’s worth stepping back to fully understand why we invest in this open-source project, which is freely available to all. Why would a for-profit firm spend time investing in this public good? Since the 2009 HITECH act incentivized EHR adoption, data has become much more ubiquitous in healthcare. Despite all that’s gone wrong in US healthcare, the fact that healthcare data is…

Levi Thatcher March 28, 2018

Many vendors deliver machine learning models with different applications in healthcare. But they don’t all deliver accurate models that are easy to implement, targeted to a specific use case, connected to actionable interventions, and surrounded by a machine learning community and support team with extensive, exclusive healthcare experience. These machine learning qualities are possible only through a machine learning model delivered by a vendor with a unique set of capabilities. There are five differentiators behind effective machine learning models and vendors:
  1. Vendor’s expertise and exclusive focus on healthcare.
  2. Machine learning model’s access to extensive data sources.
  3. Machine learning model’s ease of implementation.
  4. Machine learning model’s interpretability and buy-in.
  5. Machine learning model’s conformance with privacy standards.
These five factors separate the high-value vendors and models from the crowd, so healthcare systems can quickly implement machine learning and start seeing improvement results.
Levi Thatcher March 14, 2018

tl;dr: Healthcare needs practical machine learning tools; the focus on deep learning and GPUs doesn’t help the average health system. Background Google just released a paper called “Scalable and accurate deep learning for electronic health records” that has received deserved acclaim in both the machine learning (ML) and healthcare communities. This research comes from the Google Brain group and isn’t their first foray into healthcare. See, for example, their impressive work in diabetic retinopathy. In fact, it’s now common for tech giants to wade into…

Yannick Van Huele January 25, 2018

Last summer we discussed the simplified interface of the 1.0 CRAN release of healthcare.ai-R, and we’re now thrilled to demo new features related to clinician guidance in the 1.2 version. We’re calling this Patient Impact Predictor (PIP). Understanding an ML model This week we’d like to highlight new functionality that allows one to go a step beyond surfacing predictions to also surface targeted interventions. Risk scores are a great first step, but prescriptive guidance is where the results of machine learning (ML) may actually catch up to the…

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…

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