Synthetic data as an enabler for machine learning applications in medicine
Synthetic data generation is the process of using machine learning methods to train a model that captures the patterns in a real dataset. Then new or synthetic data can be generated from that trained model. The synthetic data does not have a one-to-one mapping to the original data or to real patient...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9619172/ https://www.ncbi.nlm.nih.gov/pubmed/36325058 http://dx.doi.org/10.1016/j.isci.2022.105331 |
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author | Rajotte, Jean-Francois Bergen, Robert Buckeridge, David L. El Emam, Khaled Ng, Raymond Strome, Elissa |
author_facet | Rajotte, Jean-Francois Bergen, Robert Buckeridge, David L. El Emam, Khaled Ng, Raymond Strome, Elissa |
author_sort | Rajotte, Jean-Francois |
collection | PubMed |
description | Synthetic data generation is the process of using machine learning methods to train a model that captures the patterns in a real dataset. Then new or synthetic data can be generated from that trained model. The synthetic data does not have a one-to-one mapping to the original data or to real patients, and therefore has the potential of privacy preserving properties. There is a growing interest in the application of synthetic data across health and life sciences, but to fully realize the benefits, further education, research, and policy innovation is required. This article summarizes the opportunities and challenges of SDG for health data, and provides directions for how this technology can be leveraged to accelerate data access for secondary purposes. |
format | Online Article Text |
id | pubmed-9619172 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-96191722022-11-01 Synthetic data as an enabler for machine learning applications in medicine Rajotte, Jean-Francois Bergen, Robert Buckeridge, David L. El Emam, Khaled Ng, Raymond Strome, Elissa iScience Perspective Synthetic data generation is the process of using machine learning methods to train a model that captures the patterns in a real dataset. Then new or synthetic data can be generated from that trained model. The synthetic data does not have a one-to-one mapping to the original data or to real patients, and therefore has the potential of privacy preserving properties. There is a growing interest in the application of synthetic data across health and life sciences, but to fully realize the benefits, further education, research, and policy innovation is required. This article summarizes the opportunities and challenges of SDG for health data, and provides directions for how this technology can be leveraged to accelerate data access for secondary purposes. Elsevier 2022-10-13 /pmc/articles/PMC9619172/ /pubmed/36325058 http://dx.doi.org/10.1016/j.isci.2022.105331 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Perspective Rajotte, Jean-Francois Bergen, Robert Buckeridge, David L. El Emam, Khaled Ng, Raymond Strome, Elissa Synthetic data as an enabler for machine learning applications in medicine |
title | Synthetic data as an enabler for machine learning applications in medicine |
title_full | Synthetic data as an enabler for machine learning applications in medicine |
title_fullStr | Synthetic data as an enabler for machine learning applications in medicine |
title_full_unstemmed | Synthetic data as an enabler for machine learning applications in medicine |
title_short | Synthetic data as an enabler for machine learning applications in medicine |
title_sort | synthetic data as an enabler for machine learning applications in medicine |
topic | Perspective |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9619172/ https://www.ncbi.nlm.nih.gov/pubmed/36325058 http://dx.doi.org/10.1016/j.isci.2022.105331 |
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