TY - JOUR
T1 - 'Rapid Learning health care in oncology' - An approach towards decision support systems enabling customised radiotherapy
AU - Lambin, Philippe
AU - Roelofs, Erik
AU - Reymen, Bart
AU - Velazquez, Emmanuel Rios
AU - Buijsen, Jeroen
AU - Zegers, Catharina M L
AU - Carvalho, Sara
AU - Leijenaar, Ralph T H
AU - Nalbantov, Georgi
AU - Oberije, Cary
AU - Scott Marshall, M.
AU - Hoebers, Frank
AU - Troost, Esther G C
AU - van Stiphout, R.
AU - Van Elmpt, Wouter
AU - Van Der Weijden, Trudy
AU - Boersma, Liesbeth
AU - Valentini, Vincenzo
AU - Dekker, Andre
PY - 2013/10
Y1 - 2013/10
N2 - PURPOSE: An overview of the Rapid Learning methodology, its results, and the potential impact on radiotherapy. MATERIAL AND RESULTS: Rapid Learning methodology is divided into four phases. In the data phase, diverse data are collected about past patients, treatments used, and outcomes. Innovative information technologies that support semantic interoperability enable distributed learning and data sharing without additional burden on health care professionals and without the need for data to leave the hospital. In the knowledge phase, prediction models are developed for new data and treatment outcomes by applying machine learning methods to data. In the application phase, this knowledge is applied in clinical practice via novel decision support systems or via extensions of existing models such as Tumour Control Probability models. In the evaluation phase, the predictability of treatment outcomes allows the new knowledge to be evaluated by comparing predicted and actual outcomes. CONCLUSION: Personalised or tailored cancer therapy ensures not only that patients receive an optimal treatment, but also that the right resources are being used for the right patients. Rapid Learning approaches combined with evidence based medicine are expected to improve the predictability of outcome and radiotherapy is the ideal field to study the value of Rapid Learning. The next step will be to include patient preferences in the decision making.
AB - PURPOSE: An overview of the Rapid Learning methodology, its results, and the potential impact on radiotherapy. MATERIAL AND RESULTS: Rapid Learning methodology is divided into four phases. In the data phase, diverse data are collected about past patients, treatments used, and outcomes. Innovative information technologies that support semantic interoperability enable distributed learning and data sharing without additional burden on health care professionals and without the need for data to leave the hospital. In the knowledge phase, prediction models are developed for new data and treatment outcomes by applying machine learning methods to data. In the application phase, this knowledge is applied in clinical practice via novel decision support systems or via extensions of existing models such as Tumour Control Probability models. In the evaluation phase, the predictability of treatment outcomes allows the new knowledge to be evaluated by comparing predicted and actual outcomes. CONCLUSION: Personalised or tailored cancer therapy ensures not only that patients receive an optimal treatment, but also that the right resources are being used for the right patients. Rapid Learning approaches combined with evidence based medicine are expected to improve the predictability of outcome and radiotherapy is the ideal field to study the value of Rapid Learning. The next step will be to include patient preferences in the decision making.
KW - Cancer
KW - Decision support system (dss)
KW - Radiotherapy
KW - Rapid learning
KW - Tailored radiation treatment
U2 - 10.1016/j.radonc.2013.07.007
DO - 10.1016/j.radonc.2013.07.007
M3 - Article
C2 - 23993399
SN - 0167-8140
VL - 109
SP - 159
EP - 164
JO - Radiotherapy and Oncology
JF - Radiotherapy and Oncology
IS - 1
ER -