Product Brief – AutoML (Automated Machine Learning) For Healthcare

ClosedLoop.ai, which placed first out of 300+ teams in the CMS AI Health Outcomes Challenge, has AutoML capabilities that are purpose-built to handle messy healthcare data and streamline model development from preprocessing to validation. Built-in model explainability reports leverage patent-pending Factor Evidence™ technology and cover everything from population-level statistics to individual patient histories. Further, AutoML is seamlessly integrated with ClosedLoop’s Enterprise Healthcare Feature Store (EHFS) and ML Ops to support data scientists with an automated end-to-end ML pipeline.

Download the product brief to learn more about ClosedLoop's AutoML and why it matters for AI-enabled healthcare organizations.

Download the Product Brief

First Name

Last Name

Email

Title

Company Name

Company Size

State

checkbox
Success!
Here is your file to download.
Oops! Something went wrong while submitting the form.

Product Brief – AutoML (Automated Machine Learning) For Healthcare

Data scientists face numerous, unique challenges when building machine learning models for healthcare. Most health data collected is never used for building predictive models that are integrated in clinical settings – only 15% of hospitals use machine learning for even limited purposes. These challenges include managing missing data, accounting for rare outcomes, tracing changes in patient health and eligibility over time, and representing multiple distinct events as singular episodes of care. Further, data scientists must validate and explain model accuracy in a healthcare context to promote adoption. To build models that are successfully integrated in clinical settings, data scientists need AutoML tools specifically designed for healthcare.

Interested in learning more about the ClosedLoop platform? Check out these related resources:

Back to all resources

See What ClosedLoop Can Do For You.

Register and get updated on events and news from ClosedLoop

We add new resources regularly. Enter your email address to get them directly in your inbox.

Submit