Over the past decade, the adoption of electronic health record (EHR) systems in hospitals has become widespread. This transformation is due to the Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009, which allocated $30 million in incentives for hospitals and physician practices to adopt EHR systems. This digital explosion in big healthcare data lends itself to modern machine learning tools that can be used for a variety of tasks such as disease detection, patient journey tracking, concept representation, patient de-identification, and data augmentation. In the past, a class of deep learning models, called convolutional neural networks (CNNs) have been successfully deployed towards a variety of disease detection tasks, including classification tasks related to interstitial lung disease and colonoscopy frames and detection of polyps and pulmonary embolisms.
These efforts have been successful because the underlying data for these tasks typically contain sufficient positive and negative examples for each detection class. In each of the example diseases/disorders above, lots of people test positive and lots also test negative. Having numerous positive and negative examples helps the machine model to learn more effectively. For disease detection problems with an imbalance in positive versus negative results, supervised learning methods like CNNs struggle to perform. For example, supervised machine learning models might struggle with a rare disease like Ebola because very few patients will test positive for it, leading to a much larger group of negatives.
Generative Adversarial Networks (GANs) are useful in these cases because they can learn to produce fake examples of the underrepresented data, better training the model. In addition to improving disease detection, GANs can be used for data de-identification, which prevents patient personal information from exposure.The Health Insurance Portability and Accountability Act of 1996 (HIPAA) Privacy Rule mandates protection of patient information, meaning that healthcare providers need to take it seriously. Data de-identification is a challenging problem in the healthcare analytics space because traditional methods are not robust enough to withstand re-identification. Namely, most current methods of de-identification can be reversed, compromising the privacy of healthcare patients’ personal records. GAN models, both in research and practice, suggest promising solutions to many of the thorny problems facing healthcare today.
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