State Farm Mutual Automobile Insurance Company

Sateesh K Nallamothu

Justin Devore

Jason Beckman

Jennifer Megargell

Ruth Anne Grosskopf

Dayne Dalpoas

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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Normal, IL, United States of America

Sateesh K Nallamothu