The use of predictive models in dynamic treatment planning

Saemundur O. Haraldsson, Ragnheidur D. Brynjolfsdottir, John R. Woodward, Kristin Siggeirsdottir, Vilmundur Gudnason

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)

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.

Original languageEnglish
Title of host publication2017 IEEE Symposium on Computers and Communications, ISCC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages242-247
Number of pages6
ISBN (Electronic)9781538616291
DOIs
Publication statusPublished - 1 Sep 2017
Event2017 IEEE Symposium on Computers and Communications, ISCC 2017 - Heraklion, Greece
Duration: 3 Jul 20177 Jul 2017

Publication series

NameProceedings - IEEE Symposium on Computers and Communications
ISSN (Print)1530-1346

Conference

Conference2017 IEEE Symposium on Computers and Communications, ISCC 2017
Country/TerritoryGreece
CityHeraklion
Period3/07/177/07/17

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

Other keywords

  • Dynamic Planing
  • Genetic Improvement of Software
  • Healthcare
  • Machine Learning
  • Prediction Models
  • Vocational Rehabilitation

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