Semantic processing of EHR data for clinical research

TL;DR

This paper introduces a semantic data virtualization approach for integrating heterogeneous EHR data, enabling flexible, on-demand data conversions to support diverse clinical research applications without extensive upfront data synchronization.

Contribution

It proposes a novel semantic virtualization layer that dynamically converts EHR data into various formats and semantics using explicit rules and reasoning, enhancing data reusability and interoperability.

Findings

Applied to real-world large-scale EHR data processing

Supports multiple clinical research data standards

Uses explicit N3 rules for semantic conversions

Abstract

There is a growing need to semantically process and integrate clinical data from different sources for clinical research. This paper presents an approach to integrate EHRs from heterogeneous resources and generate integrated data in different data formats or semantics to support various clinical research applications. The proposed approach builds semantic data virtualization layers on top of data sources, which generate data in the requested semantics or formats on demand. This approach avoids upfront dumping to and synchronizing of the data with various representations. Data from different EHR systems are first mapped to RDF data with source semantics, and then converted to representations with harmonized domain semantics where domain ontologies and terminologies are used to improve reusability. It is also possible to further convert data to application semantics and store the converted results in clinical research databases, e.g. i2b2, OMOP, to support different clinical research settings. Semantic conversions between different representations are explicitly expressed using N3 rules and executed by an N3 Reasoner (EYE), which can also generate proofs of the conversion processes. The solution presented in this paper has been applied to real-world applications that process large scale EHR data.