An Adversorial Approach to Enable Re-Use of Machine Learning Models and Collaborative Research Efforts Using Synthetic Unstructured Free-Text Medical Data.

Clicks: 198
ID: 41140
2019
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Ranked #113 of 170 articles by views in Studies in health technology and informatics

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Abstract
We leverage Generative Adversarial Networks (GAN) to produce synthetic free-text medical data with low re-identification risk, and apply these to replicate machine learning solutions. We trained GAN models to generate free-text cancer pathology reports. Decision models were trained using synthetic datasets reported performance metrics that were statistically similar to models trained using original test data. Our results further the use of GANs to generate synthetic data for collaborative research and re-use of machine learning models.
Reference Key
kasthurirathne2019anstudies Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Kasthurirathne, Suranga N;Dexter, Gregory;Grannis, Shaun J;
Journal Studies in health technology and informatics
Year 2019
DOI
10.3233/SHTI190509
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Keywords Keywords not found

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