BDLBS: An Expert-Rule-Based Generator for Synthetic Complex Longitudinal Health Data

Advances in sensor technology have led to increasingly complex temporal data, with healthcare digitalization being a prominent example. Such data hold great potential for machine learning and deep learning research. However, privacy and data quality issues make calibrating models on real datasets challenging, creating a need for realistic synthetic data. Deep learning models require large datasets and computational resources, and they do not always reproduce ground-truth dynamics faithfully. We address the generation of realistic asynchronous data combining event sequences with multivariate time series. We propose BDLBS, a simulator based on expert-defined rules, designed to fill the gap in generating realistic anesthesia datasets. The simulator is lightweight and scalable, and extensive tests demonstrate that BDLBS realistically captures the effects of medical actions on patient dynamics, along with the temporal and inter-variable dependencies in these longitudinal data. The simulator enables the easy creation of benchmarks for calibrating machine learning and deep learning models and supports the secondary use of healthcare data in research. A public, userfriendly platform allows experts to define new surgical procedures and enables other users to generate synthetic benchmarks without any code installation.