title: Distributed learning: Developing a predictive model based on data from multiple hospitals without data leaving the hospital - A real life proof of concept.
abstract: 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.
  
  
  
  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.
  
  
  
  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.
  
  
  
  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.
authors.affiliation: Department of Radiation Oncology (MAASTRO Clinic), Maastricht, The Netherlands. Electronic address: arthur.jochems@maastro.nl.
authors.forename: Arthur
authors.initials: A
authors.lastname: Jochems
authors.affiliation: Department of Radiation Oncology (MAASTRO Clinic), Maastricht, The Netherlands; GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, The Netherlands.
authors.forename: Timo M
authors.initials: TM
authors.lastname: Deist
authors.affiliation: Department of Radiation Oncology (MAASTRO Clinic), Maastricht, The Netherlands; GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, The Netherlands.
authors.forename: Johan
authors.initials: J
authors.lastname: van Soest
authors.affiliation: Klinik für Strahlentherapie (University clinic Aachen), Germany.
authors.forename: Michael
authors.initials: M
authors.lastname: Eble
authors.affiliation: Department of Radiation Oncology (Jessa Hospital), Hasselt, The Netherlands.
authors.forename: Paul
authors.initials: P
authors.lastname: Bulens
authors.affiliation: Departement de Physique Medicale (CHU de Liège), Belgium.
authors.forename: Philippe
authors.initials: P
authors.lastname: Coucke
authors.affiliation: Catharina-Hospital Eindhoven, The Netherlands.
authors.forename: Wim
authors.initials: W
authors.lastname: Dries
authors.affiliation: Department of Radiation Oncology (MAASTRO Clinic), Maastricht, The Netherlands; GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, The Netherlands.
authors.forename: Philippe
authors.initials: P
authors.lastname: Lambin
authors.affiliation: Department of Radiation Oncology (MAASTRO Clinic), Maastricht, The Netherlands.
authors.forename: Andre
authors.initials: A
authors.lastname: Dekker
mesh_terms: D001499:Bayes Theorem; D003219:Confidentiality; D057225:Data Mining / Q000941:ethics / Q000379:methods*; D004417:Dyspnea / Q000209:etiology; D005060:Europe; D005260:Female; D006801:Humans; D033181:Information Dissemination / Q000941:ethics / Q000379:methods*; D008175:Lung Neoplasms / Q000532:radiotherapy*; D008297:Male; D008962:Models, Theoretical; D057285:Precision Medicine / Q000379:methods; D011237:Predictive Value of Tests; D012372:ROC Curve; D011832:Radiation Injuries / Q000209:etiology; D011878:Radiotherapy / Q000009:adverse effects
keywords: Bayesian networks; Distributed learning; Dyspnea; Machine learning; Privacy preserving data-mining