Health Catalyst Launches Open Source Machine Learning Tool

Dec. 1, 2016
Health Catalyst, the Salt Lake City, Utah-based analytics vendor, has created healthcare.ai, a repository of machine learning algorithms that will allow healthcare professionals to use machine learning tools to build accurate models all in one central location.

Health Catalyst, the Salt Lake City, Utah-based analytics vendor, has created healthcare.ai, a repository of machine learning algorithms that will allow healthcare professionals to use machine learning tools to build accurate models all in one central location.

In a press release announcement, Health Catalyst officials noted that the use of machine learning and predictive analytics to improve health outcomes has so far been limited to highly-trained data scientists, mostly in the nation’s top academic medical centers. But the vendor’s new website will aim to make machine learning accessible to the thousands of healthcare professionals who possess little or no data science skills but who share an interest in using the technology to improve patient care. Officials attest that there is no similar platform or environment for healthcare professionals that exists today.

By making its central repository of proven machine learning algorithms freely available, healthcare.ai aims to enable a large, diverse group of technical healthcare professionals to quickly use machine learning tools to build accurate models. The healthcare.ai site provides one central spot to download algorithms and tools, read documentation, request new features, submit questions, follow the blog, and contribute code.

Officials say that healthcare.ai makes it easier to create predictive and pattern recognition models using a healthcare organization’s own data. The open source repository features packages for two common languages in healthcare data science—R and Python. These packages are designed to streamline healthcare machine learning by simplifying the workflow of creating and deploying models, and delivering functionality specific to healthcare:

  •  Pays attention to longitudinal questions
  •  Offers an easy way to do risk-adjusted comparisons
  •  Provides easy connections and deployment to databases

“Machine learning and artificial intelligence are going to transform healthcare. We are seeing amazing results and yet we are barely getting started. We are applying it to the reduction of patient harm events, care management, hospital acquired infections, revenue cycle management, patient risk stratification, and more. With machine learning, the data is talking to us, exposing insights that we’ve never seen before with traditional business intelligence and analytics,” Dale Sanders, executive vice president of Health Catalyst, which started and is contributing ongoing support to healthcare.ai, said in a statement.

She continued, “By open sourcing healthcare.ai, we hope to facilitate industrywide collaboration and advance the adoption of machine learning, making it easy for healthcare organizations to learn from and enhance these tools together, without the need for a team of data scientists. All of us have seen what open source software has achieved in other industries and we want to be a part of that in healthcare.”

Interested parties in healthcare.ai can visit the site, choose either the R or Python language, read the install instructions, and follow the examples.

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