Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/26060
Appears in Collections:Computing Science and Mathematics Journal Articles
Title: The use of predictive models in dynamic treatment planning
Authors: Haraldsson, Seamundur
Brynjolfsdottir, Ragnheidur D
Woodward, John
Siggeirsdottir, Kristin
Gudnason, Vilmundur
Keywords: Prediction Models
Healthcare
Dynamic Planing
Machine Learning
Vocational Rehabilitation
Genetic Improvement of Software
Issue Date: 4-Sep-2017
Citation: Haraldsson S, Brynjolfsdottir RD, Woodward J, Siggeirsdottir K & Gudnason V (2017) The use of predictive models in dynamic treatment planning In: 2017 IEEE Symposium on Computers and Communications (ISCC), Piscataway, NJ, USA: IEEE. IEEE Symposium on Computers and Communications (ISCC 2017), 3.7.2017 - 6.7.2017, Heraklion, Greece, pp. 242-247.
Abstract: 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.
DOI Link: http://dx.doi.org/10.1109/ISCC.2017.8024536
Rights: © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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