Distributed learning: Developing a predictive model based on data from multiple hospitals without data leaving the hospital – A real life proof of concept

Radiotherapy and Oncology

Volumn 121, Issue 3, 2016, Pages 459-467

  Jochems, Arthur ^a   Deist, Timo M ^a,b   van Soest, Johan ^a,b   Eble, Michael ^c   Bulens, Paul ^d   Coucke, Philippe ^e   Dries, Wim ^f   Lambin, Philippe ^a,b   Dekker, Andre ^a  

^a MAASTRO CLINIC   (Netherlands)

^b MAASTRICHT UNIVERSITY   (Netherlands)

^c UNIVERSITY HOSPITAL RWTH AACHEN   (Germany)

^d Departement de Physique Medicale ^*  (Netherlands)

^e Departement de Physique Medicale CHU de Liège   (Belgium)

^f CATHARINA HOSPITAL   (Netherlands)

Author keywords

Bayesian networks; Distributed learning; Dyspnea; Machine learning; Privacy preserving data mining

Indexed keywords

ARTICLE; BAYES THEOREM; CANCER PATIENT; CANCER RADIOTHERAPY; CHEMORADIOTHERAPY; DATA ANALYSIS; DYSPNEA; FEMALE; HOSPITAL; HUMAN; LUNG CANCER; LUNG CARCINOMA; MACHINE LEARNING; MALE; NON SMALL CELL LUNG CANCER; PATIENT CODING; PREDICTIVE VALIDITY; PRIORITY JOURNAL; SMALL CELL LUNG CANCER; ADVERSE EFFECTS; CLINICAL TRIAL; CONFIDENTIALITY; DATA MINING; ETHICS; EUROPE; INFORMATION DISSEMINATION; LUNG NEOPLASMS; MULTICENTER STUDY; PERSONALIZED MEDICINE; PREDICTIVE VALUE; PROCEDURES; RADIATION INJURIES; RECEIVER OPERATING CHARACTERISTIC; THEORETICAL MODEL;

BAYES THEOREM; CONFIDENTIALITY; DATA MINING; DYSPNEA; EUROPE; FEMALE; HUMANS; INFORMATION DISSEMINATION; LUNG NEOPLASMS; MALE; MODELS, THEORETICAL; PRECISION MEDICINE; PREDICTIVE VALUE OF TESTS; RADIATION INJURIES; RADIOTHERAPY; ROC CURVE;

EID: 85006046138     PISSN: 01678140     EISSN: 18790887     Source Type: Journal    
DOI: 10.1016/j.radonc.2016.10.002     Document Type: Article

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