Data Encoding For Healthcare Data Democratisation and Information Leakage Prevention

In recent years, deep learning has demonstrated remarkable success in a wide variety of fields [1], and it is expected to have a significant impact on healthcare as well [2]. Many attempts have been made to achieve this breakthrough in healthcare informatics, which often deals with noisy, heterogeneous, and non-standardized electronic health records (EHRs) [3]. However, most clinical deep learning tools are either not robust enough or have not been tested in real-world scenarios [4, 5]. Deep learning solutions, approved by regulatory bodies, are less common in healthcare informatics, which shows that deep learning hasn't had the same level of success as in other fields such as speech and image processing [6]. Along with well-known explainability challenges in deep learning models [7], the lack of data democratization [8] and latent information leakage (information leakage from trained models) [9, 10] can also be regarded as a major hindrance in the development and acceptance of robust clinical deep learning solutions. In the current context, data democratization and information leakage can be described as: Data democratization: It involves making digital healthcare data available to a wider cohort of the AI researchers.