Synthesising Electronic Health Records: Cystic Fibrosis Patient Group

Abstract

Class imbalance can often degrade predictive performance of supervised learning algorithms. Balanced classes can be obtained by oversampling exact copies, with noise, or interpolation between nearest neighbours (as in traditional SMOTE methods). Oversampling using augmentation, as is typical in computer vision tasks, can be achieved with deep generative models. Deep generative models are effective data synthesisers due to their ability to capture complex underlying distributions. Synthetic data in healthcare can enhance interoperability between healthcare providers by ensuring patient privacy. Equipped with large synthetic datasets which do well to represent small patient groups, machine learning in healthcare can address the current challenges of bias and generalisability. This paper evaluates synthetic data generators ability to synthesise patient electronic health records. We test the utility of synthetic data for patient outcome classification, observing increased predictive performance when augmenting imbalanced datasets with synthetic data.

Synthetic Data as a Proxy for Real-World Electronic Health Records in the Patient Length of Stay Prediction

“…In a recent paper by Muller et al [11], the authors take a research approach similar to this paper, examining the applicability of synthetic EHR created by GANs for the prediction of the patient outcome. However, they mainly focus on the value of the artificially created data through GANs for solving the problem of class imbalance.…”

“…Thus, similar to Section 2.4, a smaller data set is used here to address the problem of data scarcity in the healthcare domain and, thus, also answering the raised research question of Fernandes [12] (see Section 2.3). Furthermore, to the authors' knowledge, only the very recent work of Muller et al [11] examines how to overcome the problem of imbalance through the application of GANs in the context of a binary classification task. This is extended in this paper by examining this issue regarding a multi-class classification problem.…”

“…Privacy measurement of synthetic data is a topic that continues to be actively discussed in the literature. As a result, the duplicate count utilized in previous studies that are closely related to this paper (see [11]) is used as a measurement of privacy. To replicate the full variability and diversion in the original data, the generator models could simply copy the original data.…”

“…Thereby, it specifically aims to examine the creation of synthetic data as a proxy for scarce real-world data sets as this bears the potential for huge time and cost savings for the clinic and its personnel. Although there is some related research on this topic, this is mainly limited to specific patient groups, e.g., cystic fibrosis patients [11], or using large data sets, and, thus, does not address the issue of data scarcity for the creation of synthetic data [11,12]. Therefore, this work aims to close this gap by answering open research questions through the application of generator models to smaller real-world data sets to test their performance on scarce data.…”