Generation of realistic synthetic data using multimodal neural ordinary differential equations (Updated May 25, 2022): Information Technology

2022 JUN 08 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- According to news reporting based on a preprint abstract, our journalists obtained the following quote sourced from medrxiv.org:

“Individual organizations, such as hospitals, pharma companies and health insurance providers are currently limited in their ability to collect data that is fully representative of a disease population. This can in turn negatively impact the generalization ability of statistical models and scientific insights.

“However, sharing data across different organizations is highly restricted by legal regulations. While federated data access concepts exist, they are technically and organizationally difficult to realize. An alternative approach would be to exchange synthetic patient data instead.

“In this work, we introduce the Multimodal Neural Ordinary Differential Equation (MultiNODE), a hybrid, multimodal AI approach, which allows for generating highly realistic synthetic patient trajectories on a continuous time scale, hence enabling smooth interpolation and extrapolation of clinical studies.

“Our proposed method can integrate both static and longitudinal data and implicitly handles missing values. We demonstrate the capabilities of our approach by applying it to real patient-level data from two independent clinical studies and simulated epidemiological data of an infectious disease.”

This preprint has not been peer-reviewed.

For more information on this research see: http://medrxiv.org/content/10.1101/2021.09.26.21263968v2

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