On Scene Injury Severity Prediction (OSISP)

Project manager
Stefan Candefjord
Ruben Buendia
2016-01-01 - 2017-12-31


On Scene Injury Severity Prediction (OSISP) Algorithm Improvement Using Data Science

The objective was to enhance an existing OSISP algorithm in four dimensions. These dimensions are performance, validity, usability and generalizability. The approach is based on the use of big data tools and technologies. Our previous OSISP studies are based on homogeneous data from a single source (STRADA). In this project, a systematic exploration of different data analysis and machinelearning approaches has been carried out. Due to the heterogeneous data sources (STRADA and NASS-CDS), a suite of data mapping and encoding schemes was defined and implemented using efficient data storage and retrieval protocols for improved performance.

Short facts

Project title: On Scene Injury Severity Prediction (OSISP)
Project type:
Research area:
Care and rescue

Project publications

Many victims in traffic accidents do not receive optimal care due to the fact that the severity of their injuries is not realized early on. Triage protocols are based on physiological and anatomical criteria and subsequently
Buendia R, Candefjord S, Fagerlind H, Bálint A, Sjöqvist BA
Published in
Accident Analysis & Prevention
Year of publication
The aim of this study is to develop an on-scene injury severity prediction (OSISP) algorithm for truck occupants using only accident characteristics that are feasible to assess at the scene of the accident. The purpose of
Candefjord S, Buendia R, Fagerlind H, Bálint A, Wege C, Sjöqvist BA
Published in
Traffic Injury Prevention
Year of publication

Safer – Vehicle and Traffic Safety Centre

SAFER is the open research arena where researchers and expertise work together to create safe mobility. Our traffic safety approach covers people, vehicles and the infrastructure – and together we contribute to safer road transports and smarter, more sustainable cities.

Contact information


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