State Farm Mutual Automobile Insurance Company

Jennifer Megargell

Justin Devore

Jason Beckman

Dayne Dalpoas

Ruth Anne Grosskopf

Sateesh K Nallamothu

Kathleen Cordiner

Techniques described herein relate to training and executing machine learning models configured to determine injury probabilities based on vehicle accident data. In some cases, a decision tree model may be constructed including various branching criteria based on particular vehicle accident data and injury ground truth data, including leaf nodes storing corresponding injury probabilities. A model execution component may execute the trained models to determine the probability of potential injuries associated with vehicle accidents. Based on the potential injury probabilities, a vehicle accident analysis system may identify target computer systems and/or target processes to be initiated on the target systems, based on the injury probabilities. In some example, the model execution architecture may be implemented using an event-driven system and/or cloud-based data storage and services to receive, store, and process data events associated with individual vehicle accidents.

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Bloomington, IL, United States of America

Jennifer Megargell