Olivier Curé

XIMSA: extended interactive multimedia system for auto-medication

The adoption of emerging semantic Web technologies will lead computer-based medical systems to a new dimension. These systems will benefit from next generation web knowledge representation languages and reasoning facilities. This paper describes such an application which aims to provide several services related to drug consumption. The originality of this approach relies on its general-public orientation. We already have a development experience in this field with XIMSA (interactive multimedia system for auto-medication). XIMSA is a logical extension of this system which incorporates some new features based on the use of a formal Web ontology.

On the Potential Integration of an Ontology-Based Data Access Approach in NoSQL Stores

No SQL stores are emerging as an efficient alternative to relational database management systems in the context of big data. Many actors in this domain consider that to gain a wider adoption, several extensions have to be integrated. Some of them focus on the ways of proposing more schema, supporting adapted declarative query languages and providing integrity constraints in order to control data consistency and enhance data quality. We consider that these issues can be dealt with in the context of Ontology Based Data Access (OBDA). OBDA is a new data management paradigm that exploits the semantic knowledge represented in ontologies when querying data stored in a database. We provide a proof of concept of OBDAs ability to tackle these three issues in a social application related to the medical domain.

Improving the Data Quality of Drug Databases using Conditional Dependencies and Ontologies

Many health care systems and services exploit drug related information stored in databases. The poor data quality of these databases, e.g. inaccuracy of drug contraindications, can lead to catastrophic consequences for the health condition of patients. Hence it is important to ensure their quality in terms of data completeness and soundness. In the database domain, standard Functional Dependencies (FDs) and INclusion Dependencies (INDs), have been proposed to prevent the insertion of incorrect data. But they are generally not expressive enough to represent a domain-specific set of constraints. To this end, conditional dependencies, i.e. standard dependencies extended with tableau patterns containing constant values, have been introduced and several methods have been proposed for their discovery and representation. The quality of drug databases can be considerably improved by their usage. Moreover, pharmacology information is inherently hierarchical and many standards propose graph structures to represent them, e.g. the Anatomical Therapeutic Chemical classification (ATC) or OpenGalen’s terminology. In this article, we emphasize that the technologies of the Semantic Web are adapted to represent these hierarchical structures, i.e. in RDFS and OWL. We also present a solution for representing conditional dependencies using a query language defined for these graph oriented structures, namely SPARQL. The benefits of this approach are interoperability with applications and ontologies of the Semantic Web as well as a reasoning-based query execution solution to clean underlying databases.

A database trigger strategy to maintain knowledge bases developed via data migration

The mapping between databases and ontologies is an issue of importance for the creation of the Semantic Web. This is mainly due to the large amount of web data stored in databases. Our approach tackles the consideration of the dynamic aspects of relational databases in knowledge bases. This solution is of particular interest for “ontology-driven” information systems equipped with inference functionality and which require synchronization with legacy database.

WaterFowl: A Compact, Self-indexed and Inference-Enabled Immutable RDF Store

In this paper we present WaterFowl, a novel approach for the storage of RDF triples that addresses scalability issues through compression. The architecture of our prototype, largely based on the use of succinct data structures, enables the representation of triples in a self-indexed, compact manner without requiring decompression at query answering time. Moreover, it is adapted to efficiently support RDF and RDFS entailment regimes thanks to an optimized encoding of ontology concepts and properties that does not require a complete inference materialization or query reformulation. This approach implies to make a distinction between the terminological and the assertional components of the knowledge base early in the process of data preparation, i.e., preprocessing the data before storing it in our structures. The paper describes our system’s architecture and presents some preliminary results obtained from evaluations on different datasets.

Semi-automatic data migration in a self-medication knowledge-based system

Self-medication, defined as the act to treat oneself with or without drugs, is a common practice in industrial countries. A study of available computerized solutions in this field highlights that this issue has not been considered with enough attention, although they provide valuable services to both patients and health care organizations. This paper presents XIMSA, a self-medication knowledge-based system, which is supported by a database/ ontology collaboration. This collaboration is guaranteed by DBOM, an application-independent system which enables the end-user to design, enrich and maintain an ontology from an existing database. DBOM’s functionalities are presented within XIMSA’s application domain.

On The Potential Integration of an Ontology-Based Data Access Approach in NoSQL Stores

NoSQL stores are emerging as an efficient alternative to relational database management systems in the context of big data. Many actors in this domain consider that to gain a wider adoption, several extensions have to be integrated. Some of them focus on the ways of proposing more schemas, supporting adapted declarative query languages and providing integrity constraints in order to control data consistency and enhance data quality. The authors consider that these issues can be dealt with in the context of Ontology Based Data Access OBDA. OBDA is a new data management paradigm that exploits the semantic knowledge represented in ontologies when querying data stored in a database. They provide a proof of concept of OBDAs ability to tackle these three issues in a social application related to the medical domain.

Ontology-based data integration in data logistics workflows

Data integration has become an essential issue in the development of information systems, especially in the biomedical and healthcare domain. Thereby it is required to have methods and tools available to support this effort in a systematic and structured manner, i.e. to have a conceptual, model based approach. We present such an approach; it is constituted by two components which are coping with the two main challenges of data integration: Data Logistics copes with the technical task of data transmission and data exchange; an ontology-based transformation copes with the semantic issues of data integration by dealing with the heterogeneity of formats, terminologies and ontologies. This paper shows how the synergetic combination of these concepts provides an adequate solution to data integration in healthcare applications.

Ontology interaction with a patient electronic health record

The XIMSA system (extented interactive multimedia system for auto-medication) aims to propose information and services to patients on mild clinical signs and associated treatments, a domain usually called self-medication. An important number of XIMSAs services benefit from inferences which are the results of collaborations between a self-medication knowledge base (ontology and concept instances), drug and symptom databases and a patient simplified electronic health record. This paper focuses on the electronic health record/knowledge base interactions which enable to validate drug consumption considering a patient profile but also to design usable and user-friendly graphical user interfaces.

A formal concept analysis and semantic query expansion cooperation to refine health outcomes of interest.

BackgroundElectronic Health Records (EHRs) are frequently used by clinicians and researchers to search for, extract, and analyze groups of patients by defining Health Outcome of Interests (HOI). The definition of an HOI is generally considered a complex and time consuming task for health care professionals.MethodsIn our clinical note-based pharmacovigilance research, we often operate upon potentially hundreds of ontologies at once, expand query inputs, and we also increase the search space over clinical text as well as structured data. Such a method implies to specify an initial set of seed concepts, which are based on concept unique identifiers. This paper presents a novel method based on Formal Concept Analysis (FCA) and Semantic Query Expansion (SQE) to assist the end-user in defining their seed queries and in refining the expanded search space that it encompasses.ResultsWe evaluate our method over a gold-standard corpus from the 2008 i2b2 Obesity Challenge. This experimentation emphasizes positive results for sensitivity and specificity measures. Our new approach provides better recall with high precision of the obtained results. The most promising aspect of this approach consists in the discovery of positive results not present our Obesity NLP reference set.ConclusionsTogether with a Web graphical user interface, our FCA and SQE cooperation end up being an efficient approach for refining health outcome of interest using plain terms. We consider that this approach can be extended to support other domains such as cohort building tools.