Distributed learning: Developing a predictive model based on data from multiple hospitals without data leaving the hospital - A real life proof of concept
Distributed learning: Developing a predictive model based on data from multiple hospitals without data leaving the hospital - A real life proof of concept
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DOI：
10.1016/j.radonc.2016.10.002
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发表时间：
2016-12-01
影响因子：
5.7
通讯作者：
Dekker, Andre
中科院分区：
文献类型：
--
作者：
Jochems, Arthur;Deist, Timo M.;Dekker, Andre
目的：实现个性化医疗的主要障碍之一是获得足够的患者数据以提供给预测模型。合并来自多个医院的数据是困难的，因为与数据共享相关的伦理、法律、政治和行政障碍。为了避免这些问题，可以使用分布式学习方法。患者和方法：从5个不同的医疗机构收集和存储来自5个不同医疗机构的肺癌患者的临床数据(荷兰MAASTRO医院123名患者、比利时Jessa医院24名患者、比利时列日医院34名患者、德国亚琛医院48名患者和荷兰埃因霍温医院58名患者)。该模型预测呼吸困难，这是肺癌放射治疗后常见的副作用。结果：我们证明了使用分布式学习方法来训练来自多个医院的患者数据的贝叶斯网络模型是可能的，而这些数据不需要离开单个医院。模型的5次交叉验证的AUC为0.61(95%CI，0.51~0.70)，外部验证集的AUC为0.59~0.71。结论：分布式学习可以在避免数据共享障碍的同时，对来自多个医院的数据进行预测模型的学习。此外，分布式学习方法可以用于从多家医院的常规患者数据中提取和使用知识，同时符合各种国家和欧洲的隐私法。(C)2016年提交人(S)。爱思唯尔爱尔兰有限公司出版。
Purpose: One of the major hurdles in enabling personalized medicine is obtaining sufficient patient data to feed into predictive models. Combining data originating from multiple hospitals is difficult because of ethical, legal, political, and administrative barriers associated with data sharing. In order to avoid these issues, a distributed learning approach can be used. Distributed learning is defined as learning from data without the data leaving the hospital.Patients and methods: Clinical data from 287 lung cancer patients, treated with curative intent with chemoradiation (CRT) or radiotherapy (RT) alone were collected from and stored in 5 different medical institutes (123 patients at MAASTRO (Netherlands, Dutch), 24 at Jessa (Belgium, Dutch), 34 at Liege (Belgium, Dutch and French), 48 at Aachen (Germany, German) and 58 at Eindhoven (Netherlands, Dutch)).A Bayesian network model is adapted for distributed learning (watch the animation: http://youtu.bei nQpqMIuHyOk). The model predicts dyspnea, which is a common side effect after radiotherapy treatment of lung cancer.Results: We show that it is possible to use the distributed learning approach to train a Bayesian network model on patient data originating from multiple hospitals without these data leaving the individual hospital. The AUC of the model is 0.61 (95%Cl, 0.51-0.70) on a 5-fold cross-validation and ranges from 0.59 to 0.71 on external validation sets.Conclusion: Distributed learning can allow the learning of predictive models on data originating from multiple hospitals while avoiding many of the data sharing barriers. Furthermore, the distributed learning approach can be used to extract and employ knowledge from routine patient data from multiple hospitals while being compliant to the various national and European privacy laws. (C) 2016 The Author(s). Published by Elsevier Ireland Ltd.