title: 'Rapid Learning health care in oncology' - an approach towards decision support systems enabling customised radiotherapy'.
abstract: OBJECTIVE
  An overview of the Rapid Learning methodology, its results, and the potential impact on radiotherapy.
  
  
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Radiation Oncology (MAASTRO), GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, The Netherlands. Electronic address: philippe.lambin@maastro.nl.
authors.forename: Philippe
authors.initials: P
authors.lastname: Lambin
authors.forename: Erik
authors.initials: E
authors.lastname: Roelofs
authors.forename: Bart
authors.initials: B
authors.lastname: Reymen
authors.forename: Emmanuel Rios
authors.initials: ER
authors.lastname: Velazquez
authors.forename: Jeroen
authors.initials: J
authors.lastname: Buijsen
authors.forename: Catharina M L
authors.initials: CM
authors.lastname: Zegers
authors.forename: Sara
authors.initials: S
authors.lastname: Carvalho
authors.forename: Ralph T H
authors.initials: RT
authors.lastname: Leijenaar
authors.forename: Georgi
authors.initials: G
authors.lastname: Nalbantov
authors.forename: Cary
authors.initials: C
authors.lastname: Oberije
authors.forename: M
authors.initials: M
authors.lastname: Scott Marshall
authors.forename: Frank
authors.initials: F
authors.lastname: Hoebers
authors.forename: Esther G C
authors.initials: EG
authors.lastname: Troost
authors.forename: Ruud G P M
authors.initials: RG
authors.lastname: van Stiphout
authors.forename: Wouter
authors.initials: W
authors.lastname: van Elmpt
authors.forename: Trudy
authors.initials: T
authors.lastname: van der Weijden
authors.forename: Liesbeth
authors.initials: L
authors.lastname: Boersma
authors.forename: Vincenzo
authors.initials: V
authors.lastname: Valentini
authors.forename: Andre
authors.initials: A
authors.lastname: Dekker
mesh_terms: D020000:Decision Support Systems, Clinical; D019317:Evidence-Based Medicine; D006801:Humans; D007858:Learning; D009369:Neoplasms / Q000532:radiotherapy*; D057285:Precision Medicine
keywords: Cancer; Decision support system (DSS); Radiotherapy; Rapid Learning; Tailored radiation treatment