Anonymization of Electronic Health Records by the Use of Dimensionality Reduction Techniques

Electronic health records (EHR) mostly consist of highly sensitive data, whose sharing is highly regulated due to privacy and security concerns. To address privacy protections, de-identification and anonymization techniques offer a more secure way of data transmission to ensure patient's privacy. Principal components analysis (PCA) has emerged as an anonymization technique. The applicability of more diverse and complex dimensionality reduction (DR) techniques such as Factor Analysis of Mixed Data (FAMD), or Autoencoder (AE), remains largely unexplored. The objective of this study is an investigation into the potential of FAMD and AE as anonymizers for EHR. The goal of this study is to apply advanced DR methods on three EHR datasets of varying sizes. Subsequently, a supervised prediction task is performed on the anonymized EHR using an artificial neural network (ANN), and the performance of the prediction task is compared to the performance of the original, non-anonymized EHR. The findings indicate that the use of FAMD and AE in the anonymization of EHR results in a comparable performance to that of the original, non-anonymized EHR in a downstream prediction task. This study thus indicates the potential of advanced DR as an anonymization technique for EHR, thereby underscoring the necessity for further investigation.