Health Catalyst Launches Open Source Machine Learning Tool | Healthcare Informatics Magazine | Health IT | Information Technology Skip to content Skip to navigation

Health Catalyst Launches Open Source Machine Learning Tool

December 1, 2016
by Rajiv Leventhal
| Reprints

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.

Get the latest information on Health IT and attend other valuable sessions at this two-day Summit providing healthcare leaders with educational content, insightful debate and dialogue on the future of healthcare and technology.

Learn More

Topics

News

Study will Leverage Connecticut HIE to Help Prevent Suicides

A new study will aim to leverage CTHealthLink, a physician-led health information exchange (HIE) in Connecticut, to help identify the factors leading to suicide and to ultimately help prevent those deaths.

Duke Health First to Achieve HIMSS Stage 7 Rating in Analytics

North Carolina-based Duke Health has become the first U.S. healthcare institution to be awarded the highest honor for analytic capabilities by HIMSS Analytics.

NIH Releases First Dataset from Adolescent Brain Development Study

The National Institutes of Health (NIH) announced the release of the first dataset from the Adolescent Brain Cognitive Development (ABCD) study, which will enable scientists to conduct research on the many factors that influence brain, cognitive, social, and emotional development.

Boston Children's Accelerates Data-Driven Approach to Clinical Research

In an effort to bring a more data-driven approach to clinical research, Boston Children’s Hospital has joined the TriNetX global health research network.

Paper Records, Films Most Common Type of Healthcare Data Breach, Study Finds

Despite the high level of hospital adoption of electronic health records and federal incentives to do so, paper and films were the most frequent location of breached data in hospitals, according to a recent study.

AHA Appoints Senior Advisor for Cybersecurity and Risk

The American Hospital Association (AHA) has announced that John Riggi has joined the association as senior advisor for cybersecurity and risk.