Browsing by Author "Dexter, Gregory"

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    An Adversorial Approach to Enable Re-Use of Machine Learning Models and Collaborative Research Efforts Using Synthetic Unstructured Free-Text Medical Data
    (IOS, 2019) Kasthurirathne, Suranga N.; Dexter, Gregory; Grannis, Shaun J.; Epidemiology, School of Public Health
    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.
  • Thumbnail Image
    Item
    Generalization of Machine Learning Approaches to Identify Notifiable Diseases Reported from a Statewide Health Information Exchange
    (MEDINFO Conference proceedings, 2019-08-25) Dexter, Gregory; Kasthurirathne, Suranga; Dixon, Brian E.; Grannis, Shaun
  • Thumbnail Image
    Item
    Generative Adversarial Networks for Creating Synthetic Free-Text Medical Data: A Proposal for Collaborative Research and Re-use of Machine Learning Models
    (AMIA Informatics summit 2021 Conference Proceedings., 2021-03) Kasthurirathne, Suranga N.; Dexter, Gregory; Grannis, Shaun J.
    Restrictions in sharing Patient Health Identifiers (PHI) limit cross-organizational re-use of free-text medical data. We leverage Generative Adversarial Networks (GAN) to produce synthetic unstructured free-text medical data with low re-identification risk, and assess the suitability of these datasets to replicate machine learning models. We trained GAN models using unstructured free-text laboratory messages pertaining to salmonella, and identified the most accurate models for creating synthetic datasets that reflect the informational characteristics of the original dataset. Natural Language Generation metrics comparing the real and synthetic datasets demonstrated high similarity. Decision models generated using these datasets reported high performance metrics. There was no statistically significant difference in performance measures reported by models trained using real and synthetic datasets. Our results inform the use of GAN models to generate synthetic unstructured free-text data with limited re-identification risk, and use of this data to enable collaborative research and re-use of machine learning models.