TY - GEN
T1 - The use of predictive models in dynamic treatment planning
AU - Haraldsson, Saemundur O.
AU - Brynjolfsdottir, Ragnheidur D.
AU - Woodward, John R.
AU - Siggeirsdottir, Kristin
AU - Gudnason, Vilmundur
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/9/1
Y1 - 2017/9/1
N2 - With the expanding load on healthcare and consequent strain on budget, the demand for tools to increase efficiency in treatments is rising. The use of prediction models throughout the treatment to identify risk factors might be a solution. In this paper we present a novel implementation of a prediction tool and the first use of a dynamic predictor in vocational rehabilitation practice. The tool is periodically updated and improved with Genetic Improvement of software. The predictor has been in use for 10 months and is evaluated on predictions made during that time by comparing them with actual treatment outcome. The results show that the predictions have been consistently accurate throughout the patients' treatment. After approximately 3 week learning phase, the predictor classified patients with 100% accuracy and precision on previously unseen data. The predictor is currently being successfully used in a complex live system where specialists have used it to make informed decisions.
AB - With the expanding load on healthcare and consequent strain on budget, the demand for tools to increase efficiency in treatments is rising. The use of prediction models throughout the treatment to identify risk factors might be a solution. In this paper we present a novel implementation of a prediction tool and the first use of a dynamic predictor in vocational rehabilitation practice. The tool is periodically updated and improved with Genetic Improvement of software. The predictor has been in use for 10 months and is evaluated on predictions made during that time by comparing them with actual treatment outcome. The results show that the predictions have been consistently accurate throughout the patients' treatment. After approximately 3 week learning phase, the predictor classified patients with 100% accuracy and precision on previously unseen data. The predictor is currently being successfully used in a complex live system where specialists have used it to make informed decisions.
KW - Dynamic Planing
KW - Genetic Improvement of Software
KW - Healthcare
KW - Machine Learning
KW - Prediction Models
KW - Vocational Rehabilitation
UR - http://www.scopus.com/inward/record.url?scp=85030561482&partnerID=8YFLogxK
U2 - 10.1109/ISCC.2017.8024536
DO - 10.1109/ISCC.2017.8024536
M3 - Conference contribution
AN - SCOPUS:85030561482
T3 - Proceedings - IEEE Symposium on Computers and Communications
SP - 242
EP - 247
BT - 2017 IEEE Symposium on Computers and Communications, ISCC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE Symposium on Computers and Communications, ISCC 2017
Y2 - 3 July 2017 through 7 July 2017
ER -