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

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 shari...

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Autores principales: Lopes, Ricardo R., Mamprin, Marco, Zelis, Jo M., Tonino, Pim A. L., van Mourik, Martijn S., Vis, Marije M., Zinger, Svitlana, de Mol, Bas A. J. M., de With, Peter H. N., Marquering, Henk A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8632813/
https://www.ncbi.nlm.nih.gov/pubmed/34869698
http://dx.doi.org/10.3389/fcvm.2021.787246
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author Lopes, Ricardo R.
Mamprin, Marco
Zelis, Jo M.
Tonino, Pim A. L.
van Mourik, Martijn S.
Vis, Marije M.
Zinger, Svitlana
de Mol, Bas A. J. M.
de With, Peter H. N.
Marquering, Henk A.
author_facet Lopes, Ricardo R.
Mamprin, Marco
Zelis, Jo M.
Tonino, Pim A. L.
van Mourik, Martijn S.
Vis, Marije M.
Zinger, Svitlana
de Mol, Bas A. J. M.
de With, Peter H. N.
Marquering, Henk A.
author_sort Lopes, Ricardo R.
collection PubMed
description 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.
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spelling pubmed-86328132021-12-02 Local and Distributed Machine Learning for Inter-hospital Data Utilization: An Application for TAVI Outcome Prediction Lopes, Ricardo R. Mamprin, Marco Zelis, Jo M. Tonino, Pim A. L. van Mourik, Martijn S. Vis, Marije M. Zinger, Svitlana de Mol, Bas A. J. M. de With, Peter H. N. Marquering, Henk A. Front Cardiovasc Med Cardiovascular Medicine 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. Frontiers Media S.A. 2021-11-12 /pmc/articles/PMC8632813/ /pubmed/34869698 http://dx.doi.org/10.3389/fcvm.2021.787246 Text en Copyright © 2021 Lopes, Mamprin, Zelis, Tonino, van Mourik, Vis, Zinger, de Mol, de With and Marquering. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Cardiovascular Medicine
Lopes, Ricardo R.
Mamprin, Marco
Zelis, Jo M.
Tonino, Pim A. L.
van Mourik, Martijn S.
Vis, Marije M.
Zinger, Svitlana
de Mol, Bas A. J. M.
de With, Peter H. N.
Marquering, Henk A.
Local and Distributed Machine Learning for Inter-hospital Data Utilization: An Application for TAVI Outcome Prediction
title Local and Distributed Machine Learning for Inter-hospital Data Utilization: An Application for TAVI Outcome Prediction
title_full Local and Distributed Machine Learning for Inter-hospital Data Utilization: An Application for TAVI Outcome Prediction
title_fullStr Local and Distributed Machine Learning for Inter-hospital Data Utilization: An Application for TAVI Outcome Prediction
title_full_unstemmed Local and Distributed Machine Learning for Inter-hospital Data Utilization: An Application for TAVI Outcome Prediction
title_short Local and Distributed Machine Learning for Inter-hospital Data Utilization: An Application for TAVI Outcome Prediction
title_sort local and distributed machine learning for inter-hospital data utilization: an application for tavi outcome prediction
topic Cardiovascular Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8632813/
https://www.ncbi.nlm.nih.gov/pubmed/34869698
http://dx.doi.org/10.3389/fcvm.2021.787246
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