On Scene Injury Severity Prediction (OSISP)

Period

1 January 2016–31 December 2017

Project manager

Stefan Candefjord

Ruben Buendia

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

Research area

Post crash

Financier(s)

Chalmers University of Technology

Project type

SAFER connected project

Related publications

On scene injury severity prediction (OSISP) algorithm for car occupants

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 on mechanisms of injury in

Authors : Buendia R, Candefjord S, Fagerlind H, Bálint A, Sjöqvist BA

Year : 2015

Published in : Accident Analysis & Prevention

On-Scene Injury Severity Prediction (OSISP) Algorithm for Truck Occupants

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 developing this algorithm is to

Authors : Candefjord S, Buendia R, Fagerlind H, Bálint A, Wege C, Sjöqvist BA

Year : 2015

Published in : Traffic Injury Prevention