Using an Ontology to Derive a Sharable and Interoperable Relational Data Model for Heterogeneous Healthcare Data and Various Applications

Abstract

Background. A large volume of heavily fragmented data is generated daily in different healthcare contexts and is stored using various structures with different semantics. This fragmentation and heterogeneity make secondary use of data a challenge. Data integration approaches that derive a common data model from sources or requirements have some advantages. However, these approaches are often built for a specific application where the research questions are known. Thus, the semantic and structural reconciliation is often not reusable nor reproducible. A recent integration approach using knowledge models has been developed with ontologies that provide a strong semantic foundation. Nonetheless, deriving a data model that captures the richness of the ontology to store data with its full semantic remains a challenging task. Objectives. This paper addresses the question: How to design a sharable and interoperable data model for storing heterogeneous healthcare data and its semantic to support various applications? Method. This paper describes a method using an ontological knowledge model to automatically generate a data model for a domain of interest. The model can then be implemented in a relational database which efficiently enables the collection, storage, and retrieval of data while keeping semantic ontological annotations so that the same data can be extracted for various applications for further processing. Results. This paper (1) presents a comparison of existing methods for generating a relational data model from an ontology using 23 criteria, (2) describes standard conversion rules, and (3) presents , a prototype developed to demonstrate the conversion rules. Conclusion. This work is a first step towards automating and refining the generation of sharable and interoperable relational data models using ontologies with a freely available tool. The remaining challenges to cover all the ontology richness in the relational model are pointed out.

Building ontology-based temporal databases for data reuse: An applied example on hospital organizational structures

“…The framework is built by combining two modelling approaches: Ontorelα, the ontology to relational conversion method 16 and UHF, the unified historicization framework for temporal data modelling. 27 The integration of Ontorelα and UHF is based on a common data model, the relational data model.…”

“…The OntoRel can be implemented in a relational database system to efficiently collect, store, and retrieve data with the ontological annotations for various applications. For formal details about the conversion rules and process, see 16.…”

“…The reason for designing this ontology was threefold: (i) provide a shareable definition of the organizational structures of French hospitals (ii) support reasoning about these structures including for consistency checks, 45 (iii) benefit from the pipeline already developed to translate ontologies into relational data model. 16 It should be noted that Ontorelα and the temporal approach presented here are neutral regarding the content of the ontology used, so the goal was not to focus on a global representation of the structures, but rather to model a correct representation that highlights the importance of the temporal data model in our case study.…”

“…14 First, including temporal concepts in an ontology requires substantial design work, decreases reasoning efficiency and may cause semantic errors due to the complexity of axioms. 9,15 Second, designing a data model using an ontology requires formal algorithms to guarantee semantic conservation 11,16 ; and third, modelling and implementing a temporal data model in the database require advanced understanding of temporal representation, programming skills and design tools. 17,18 Several ontologies of time have been defined, including the OWL-Time ontology developed by the World Wide Web Consortium, 19 the SWRL Temporal Ontology 20 and SOWL, 21 the Clinical Narrative Temporal Relation Ontology 22 and TimeML.…”

Medical Data Transformations in Healthcare Systems with the Use of Natural Language Processing Algorithms

“…Different types of approaches may not perform as accurately as desired in the field of healthcare, or perform better or worse on different types of data [1][2][3][4]. In addition to that, existing solutions cannot always be applied efficiently due to the lack of common data schema for healthcare organizations to rely on [5][6][7]. This requires conversion between different data formats, and the rarity of solutions that automate those things make it a serious obstacle.…”