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Local and Distributed Machine Learning for Inter-hospital Data Utilization: An Application for TAVI Outcome Prediction

Authors :
Ricardo R. Lopes
Marco Mamprin
Jo M. Zelis
Pim A. L. Tonino
Martijn S. van Mourik
Marije M. Vis
Svitlana Zinger
Bas A. J. M. de Mol
Peter H. N. de With
Henk A. Marquering
Graduate School
ACS - Heart failure & arrhythmias
Radiology and Nuclear Medicine
Cardiology
ACS - Atherosclerosis & ischemic syndromes
ACS - Pulmonary hypertension & thrombosis
ACS - Microcirculation
APH - Aging & Later Life
Cardiothoracic Surgery
Amsterdam Neuroscience - Neurovascular Disorders
Amsterdam Neuroscience - Brain Imaging
Source :
Frontiers in cardiovascular medicine, 8. Frontiers Media S.A., Frontiers in Cardiovascular Medicine, Vol 8 (2021), Frontiers in Cardiovascular Medicine
Publication Year :
2021

Abstract

Background: Machine learning models have been developed for numerous medical prognostic purposes. These models are commonly developed using data from single centers or regional registries. Including data from multiple centers improves robustness and accuracy of prognostic models. However, data sharing between multiple centers is complex, mainly because of regulations and patient privacy issues.Objective: We aim to overcome data sharing impediments by using distributed ML and local learning followed by model integration. We applied these techniques to develop 1-year TAVI mortality estimation models with data from two centers without sharing any data.Methods: A distributed ML technique and local learning followed by model integration was used to develop models to predict 1-year mortality after TAVI. We included two populations with 1,160 (Center A) and 631 (Center B) patients. Five traditional ML algorithms were implemented. The results were compared to models created individually on each center.Results: The combined learning techniques outperformed the mono-center models. For center A, the combined local XGBoost achieved an AUC of 0.67 (compared to a mono-center AUC of 0.65) and, for center B, a distributed neural network achieved an AUC of 0.68 (compared to a mono-center AUC of 0.64).Conclusion: This study shows that distributed ML and combined local models techniques, can overcome data sharing limitations and result in more accurate models for TAVI mortality estimation. We have shown improved prognostic accuracy for both centers and can also be used as an alternative to overcome the problem of limited amounts of data when creating prognostic models.

Details

Language :
English
ISSN :
2297055X
Database :
OpenAIRE
Journal :
Frontiers in cardiovascular medicine, 8. Frontiers Media S.A., Frontiers in Cardiovascular Medicine, Vol 8 (2021), Frontiers in Cardiovascular Medicine
Accession number :
edsair.doi.dedup.....2fd4e19ae045473fa86de3bfaaf3fc6c