TY - GEN
T1 - Predicting changes in quality of life for patients in vocational rehabilitation
AU - Haraldsson, Saemundur O.
AU - Brynjolfsdottir, Ragnheidur D.
AU - Gudnason, Vilmundur
AU - Tomasson, Kristinn
AU - Siggeirsdottir, Kristin
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/6/26
Y1 - 2018/6/26
N2 - Adaptive systems will become increasingly important for health care in coming years as costs and workload grow. The need for efficient rehabilitation will expand which will be fulfilled by information technologies. This paper presents a novel implementation and application of a dynamic prediction software in vocational rehabilitation. The software is made adaptable with a Genetic Improvement of software methodology and utilised to predict fluctuations in patient's perceived quality of life. Results of accuracy, recall and precision were better than 90% for the classification of the shifts and the mean absolute error in predictions of the quantity of the shifts was low. The findings of the present study support that it is possible to predict fluctuations in quality of life on average based on the status six months prior. Professionals could therefore intervene accordingly and increase the possibility of successful rehabilitation. The significant long term effect on health care from applying the prediction tool might be reduced cost and overall improved quality of life.
AB - Adaptive systems will become increasingly important for health care in coming years as costs and workload grow. The need for efficient rehabilitation will expand which will be fulfilled by information technologies. This paper presents a novel implementation and application of a dynamic prediction software in vocational rehabilitation. The software is made adaptable with a Genetic Improvement of software methodology and utilised to predict fluctuations in patient's perceived quality of life. Results of accuracy, recall and precision were better than 90% for the classification of the shifts and the mean absolute error in predictions of the quantity of the shifts was low. The findings of the present study support that it is possible to predict fluctuations in quality of life on average based on the status six months prior. Professionals could therefore intervene accordingly and increase the possibility of successful rehabilitation. The significant long term effect on health care from applying the prediction tool might be reduced cost and overall improved quality of life.
KW - Computational intelligence
KW - Icelandic quality of life
KW - Prediction models
KW - Vocational rehabilitation
UR - http://www.scopus.com/inward/record.url?scp=85050197485&partnerID=8YFLogxK
U2 - 10.1109/EAIS.2018.8397182
DO - 10.1109/EAIS.2018.8397182
M3 - Conference contribution
AN - SCOPUS:85050197485
T3 - 2018 IEEE International Conference on Evolving and Adaptive Intelligent Systems, EAIS 2018
SP - 1
EP - 8
BT - 2018 IEEE International Conference on Evolving and Adaptive Intelligent Systems, EAIS 2018
A2 - Manolopoulos, Yannis
A2 - Iliadis, Lazaros
A2 - Angelov, Plamen
A2 - Lughofer, Edwin
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 11th IEEE International Conference on Evolving and Adaptive Intelligent Systems, EAIS 2018
Y2 - 25 May 2018 through 27 May 2018
ER -