# GAN-Based Approaches for Generating Structured Data in the Medical Domain
## 1 Introduction  
Synthetic data generation has gained importance in healthcare applications in recent years [1, 2, 3, 4]. It has become of particular interest in the medical field for two main reasons. First, real world personal data are usually captured and managed in accordance with general privacy regulations, such as the General Data Protection Regulation (GDPR) [5]. As a result, such data are hardly accessible to everyone. However, they may be shared with permission and only under strict conditions and regulations [6]. In most cases, for example, data sharing is subject to a contract between the data-providing and data-consuming institutions. This complicates the process of data acquisition and poses a major challenge for research groups, as they need access to such personal data for developing algorithms or testing their applicability to different datasets. For this reason, many data scientists and researchers rely on publicly available medical data to perform analyses and to develop innovative tools and methods [7, 8, 9, 10]. Artificially generated synthetic data alternatively offer the opportunity to improve the current situation by providing health-related data with characteristics that are similar to those of the real world data, yet not associated with any real patients. Second, for diseases that affect very few patients, i.e., rare diseases, there exist insufficient data, even for diagnosis and treatment. According to the EU definition, a disease is considered rare when it occurs in less than 1 in 2000 people [11]. Novel artificialintelligence algorithms and advanced deep learning methods, on the other hand, place high demands on the amount of data. The accuracy of such models indeed outperforms other conventional statistical and machine learning methods when a large dataset is available to them [12]. Synthetic data generation in the medical field can bridge this data scarcity by providing a large set of artificial data that represent the statistical properties of the original real data. Moreover, synthetic data can be utilized to facilitate the care of patients with rare diseases by referring them more efficiently to experienced medical experts. This is of great importance, because those experts are also rare, i.e., not available in every medical center. In recent years, numerous methods and software have been developed for synthetic data generation [13; 14; 15]. The number of emerging tools being used in healthcare and medical science is also growing rapidly; examples include: SDV (synthetic data vault) [16], SynSys [1], Synthea [17], and Synthia [18]. Some methods leverage statistical methods, e.g., R packages such as synthpop [19] and simPop [20], while others rely on more advanced artificial neural networks. Two most important and popular families of such deep generative models are Variational Autoencoders (VAEs) [21; 22] and Generative Adversarial Networks (GANs) [12; 23]. VAEs are similar to autoencoders, which additionally employ variational inference to regularize the encoding distribution and ensure that the generation of new data is less prone to overfitting. VAE models, along with other generative models, have been widely used to bridge data scarcity and to improve machine learning performance in various application domains through data augmentation, a technique that increases the amount of data by adding slightly modified versions of existing data. For example, VAEs have been developed to generate synthetic eye-tracking data from a small dataset with a variety of technological applications [24]. They were also utilized to simulate the longitudinal clinical data of virtual patients while preserving privacy, which helps scientists to conduct counterfactual studies [25]. GANs are another class of deep generative models introduced by [PERSON] et al. that have gained wide popularity and interest in many different application areas [26; 27]. A GAN model consists of two networks, namely a generator and a discriminator.