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.
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.
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