Section: New Software and Platforms

MedAnalytics

Keywords: Big data - Predictive analytics - Distributed systems

Functional Description: We implemented a method for the automatic detection of at-risk profiles based on a fine-grained analysis of prescription data at the time of admission. The system relies on an optimized distributed architecture adapted for processing very large volumes of medical records and clinical data. We conducted practical experiments with real data of millions of patients and hundreds of hospitals. We demonstrated how the various perspectives of big data improve the detection of at-risk patients, making it possible to construct predictive models that benefit from volume and variety. This prototype implementation is described in the 2017 preprint available at: https://hal.inria.fr/hal-01517087/document.

• Participants: Pierre Genevès and Thomas Calmant

• Partner: CHU Grenoble

• Contact: Pierre Genevès

• Publication: Scalable Machine Learning for Predicting At-Risk Profiles Upon Hospital Admission