Case Study: University of Michigan Center for Integrative Research in Critical Care (MCIRCC) achieves faster, more accurate life-saving predictive models with DataRobot

University of Michigan Center for Integrative Research in Critical Care (MCIRCC) - Customer Case Study

The University of Michigan Center for Integrative Research in Critical Care (MCIRCC), led in part by analytics architect Dr. Ashwin Belle, needed to turn massive, fast-moving clinical monitoring data into accurate, scalable, and explainable predictive models. Traditional data science workflows were too slow and complex, models struggled with real‑time accuracy and scalability, and communication gaps existed between data scientists, engineers, and clinicians — so MCIRCC evaluated and selected DataRobot’s automated machine learning platform to help solve these challenges.

With DataRobot, MCIRCC reduced model development and implementation time from one–two months to about two days, used the platform’s advanced data exploration to surface relevant signals, and combined DataRobot with signal and image processing to build real‑time predictive models. DataRobot-enabled models — including one that predicts hemodynamic instability well before traditional vital signs — have increased model throughput and earlier clinical alerts, while DataRobot’s explainability and deployment capabilities are speeding clinical validation and regulatory readiness toward wider, potentially FDA‑approved, deployment.

University of Michigan Center for Integrative Research in Critical Care (MCIRCC)

Ashwin Belle

Analytics Architect