Distributed learning: Developing a predictive model based on data from multiple hospitals without data leaving the hospital – A real life proof of concept. Issue 3 (December 2016)

• Record Type: Journal Article
• Authors: Jochems, Arthur; Deist, Timo M.; van Soest, Johan; Eble, Michael; Bulens, Paul; Coucke, Philippe; Dries, Wim; Lambin, Philippe; Dekker, Andre
• Is Part Of: Radiotherapy and oncology. Volume 121:Issue 3(2016:Dec.)
• Page Start: 459
• Page End: 467
• Publication Date: 2016-12
• Subjects: Bayesian networks -- Distributed learning -- Privacy preserving data-mining -- Dyspnea -- Machine learning; Oncology -- Periodicals; Radiotherapy -- Periodicals; Tumors -- Periodicals; Medical Oncology -- Periodicals; Neoplasms -- radiotherapy -- Periodicals; Radiotherapy -- Periodicals; Radiothérapie -- Périodiques; Cancérologie -- Périodiques; Tumeurs -- Périodiques; Electronic journals; 616.9940642
• DOI: 10.1016/j.radonc.2016.10.002
• Languages: English
• ISSNs: 0167-8140

Abstract

Abstract: 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.be/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%CI, 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.