Conditional Generation of Medical Time Series for Extrapolation to Underrepresented Populations

TL;DR

This paper introduces HealthGen, a method for generating synthetic electronic health records conditioned on patient characteristics, improving data diversity and model generalizability for underrepresented populations.

Contribution

HealthGen is a novel approach that accurately generates synthetic EHRs conditioned on patient features, enhancing data diversity and model robustness.

Findings

HealthGen produces more faithful synthetic EHRs than existing methods.

Augmenting data with HealthGen improves model generalizability to underrepresented groups.

Synthetic EHRs can increase data accessibility and inclusivity.

Abstract

The widespread adoption of electronic health records (EHRs) and subsequent increased availability of longitudinal healthcare data has led to significant advances in our understanding of health and disease with direct and immediate impact on the development of new diagnostics and therapeutic treatment options. However, access to EHRs is often restricted due to their perceived sensitive nature and associated legal concerns, and the cohorts therein typically are those seen at a specific hospital or network of hospitals and therefore not representative of the wider population of patients. Here, we present HealthGen, a new approach for the conditional generation of synthetic EHRs that maintains an accurate representation of real patient characteristics, temporal information and missingness patterns. We demonstrate experimentally that HealthGen generates synthetic cohorts that are significantly more faithful to real patient EHRs than the current state-of-the-art, and that augmenting real data sets with conditionally generated cohorts of underrepresented subpopulations of patients can significantly enhance the generalisability of models derived from these data sets to different patient populations. Synthetic conditionally generated EHRs could help increase the accessibility of longitudinal healthcare data sets and improve the generalisability of inferences made from these data sets to underrepresented populations.