Department of Radiation Oncology (MAASTRO), GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, The Netherlands.
Radiother Oncol. 2013 Oct;109(1):159-64. doi: 10.1016/j.radonc.2013.07.007. Epub 2013 Aug 28.
An overview of the Rapid Learning methodology, its results, and the potential impact on radiotherapy.
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.
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.
概述快速学习方法及其结果，以及对放射治疗的潜在影响。
快速学习方法分为四个阶段。在数据阶段，收集了有关过去患者、治疗方法和结果的各种数据。支持语义互操作性的创新信息技术使分布式学习和数据共享成为可能，而不会给医疗保健专业人员带来额外负担，也不需要将数据带出医院。在知识阶段，通过将机器学习方法应用于数据，为新数据和治疗结果开发预测模型。在应用阶段，通过新型决策支持系统或对肿瘤控制概率模型等现有模型进行扩展，将这些知识应用于临床实践。在评估阶段，通过比较预测和实际结果，对治疗结果的可预测性进行评估，从而验证新知识。
个性化或定制化癌症治疗不仅确保患者接受最佳治疗，还确保为合适的患者使用正确的资源。快速学习方法与循证医学相结合，有望提高结果的可预测性，放射治疗是研究快速学习价值的理想领域。下一步将是在决策中纳入患者的偏好。
