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

Keeping track of data semantics and data changes in the databases is essential to support retrospective studies and the reproducibility of longitudinal clinical analysis by preventing false conclusions from being drawn from outdated data. A knowledge model combined with a temporal model plays an essential role in organizing the data and improving query expressiveness across time and multiple institutions. This paper presents a modelling framework for temporal relational databases using an ontology to derive a shareable and interoperable data model. The framework is based on: OntoRela an ontology-driven database modelling approach and Unified Historicization Framework a temporal database modelling approach. The method was applied to hospital organizational structures to show the impact of tracking organizational changes on data quality assessment, healthcare activities and data access rights. The paper demonstrated the usefulness of an ontology to provide a formal, interoperable, and reusable definition of entities and their relationships, as well as the adequacy of the temporal database to store, trace, and query data over time.

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

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 their full semantic remains a challenging task. OBJECTIVES: This article addresses the following question: How to design a sharable and interoperable data model for storing heterogeneous healthcare data and their semantic to support various applications? METHOD: This article 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 article (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 O n t o R e l a , a prototype developed to demonstrate the conversion rules. CONCLUSION: This work is a first step toward 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.

Solubilization of cyclosporin A in dextran-g-polyethyleneglycolalkyl ether polymeric micelles.

Solubilization of the poorly water-soluble drug, Cyclosporin A (CsA), in aqueous dispersions of dextran-grafted-polyethyleneglycolalkyl ether (DEX-g-PEG-Cn) polymeric micelles was examined as a function of copolymer structure. In aqueous solution, DEX-g-PEG-Cn form polymeric micelles of low critical association concentrations (CAC) and small micelle sizes as determined by fluorescence spectroscopy and dynamic light scattering (DLS). Copolymers with longer polysaccharide chain showed larger CAC and mean diameter. The percentage of CsA loading into micelles was determined by high performance liquid chromatography. It was significantly larger in polymeric micelles compared to unmodified dextrans. It increased with increasing number of PEG-Cn units grafted per dextran chain and decreasing dextran molecular weight. The cytotoxicity of DEX-g-PEG-C(16) polymeric micelles towards Caco-2 cells, tested by MTT cytotoxicity assay, was significantly lower than that of free PEG-C(16) molecules. It can be concluded that the length of the hydrophilic part as well as the content and chemical nature of the hydrophobic substituents have an important effect on the ability of polymeric micelles to solubilize poorly-water soluble drugs.