Ali Anil Sinaci

A federated semantic metadata registry framework for enabling interoperability across clinical research and care domains

In order to enable secondary use of Electronic Health Records (EHRs) by bridging the interoperability gap between clinical care and research domains, in this paper, a unified methodology and the supporting framework is introduced which brings together the power of metadata registries (MDR) and semantic web technologies. We introduce a federated semantic metadata registry framework by extending the ISO/IEC 11179 standard, and enable integration of data element registries through Linked Open Data (LOD) principles where each Common Data Element (CDE) can be uniquely referenced, queried and processed to enable the syntactic and semantic interoperability. Each CDE and their components are maintained as LOD resources enabling semantic links with other CDEs, terminology systems and with implementation dependent content models; hence facilitating semantic search, much effective reuse and semantic interoperability across different application domains. There are several important efforts addressing the semantic interoperability in healthcare domain such as IHE DEX profile proposal, CDISC SHARE and CDISC2RDF. Our architecture complements these by providing a framework to interlink existing data element registries and repositories for multiplying their potential for semantic interoperability to a greater extent. Open source implementation of the federated semantic MDR framework presented in this paper is the core of the semantic interoperability layer of the SALUS project which enables the execution of the post marketing safety analysis studies on top of existing EHR systems.

A semantic backend for content management systems

The users of a content repository express the semantics they have in mind while defining the content items and their properties, and forming them into a particular hierarchy. However, this valuable semantics is not formally expressed, and hence cannot be used to discover meaningful relationships among the content items in an automated way. Although the need is apparent, there are several challenges in explicating this semantics in a fully automated way: first, it is difficult to distinguish between data and the metadata in the repository and secondly, not all the metadata defined, such as the file size or encoding type, contribute to the meaning. More importantly, for the developed solution to have practical value, it must address the constraints of the content management system (CMS) industry: CMS industry cannot change their repositories in production use and they need a generic solution not limited to a specific repository architecture. In this article, we address all these challenges through a set of tools developed which first semi-automatically explicate the content repository semantics to a knowledge-base and establish semantic bridges between this backend knowledge-base and the content repository. The repository content is dynamic; to be able to maintain the content repository semantics while new content is created, the changes in the repository semantics are reflected onto the knowledge-base through the semantic bridges. The tool set is complemented with a search engine that make use of the explicated semantics.

A framework for evaluating and utilizing medical terminology mappings.

Use of medical terminologies and mappings across them are considered to be crucial pre-requisites for achieving interoperable eHealth applications. Built upon the outcomes of several research projects, we introduce a framework for evaluating and utilizing terminology mappings that offers a platform for i) performing various mappings strategies, ii) representing terminology mappings together with their provenance information, and iii) enabling terminology reasoning for inferring both new and erroneous mappings. We present the results of the introduced framework from SALUS project where we evaluated the quality of both existing and inferred terminology mappings among standard terminologies.

Semantic content management and integration with JCR/CMIS compliant content repositories

Existing content management systems (CMSes) usually do not offer flexible, customizable means to create semantic, domain specific indexing and search mechanisms. Therefore, they either do not provide any semantic retrieval, search, browsing functionalities at all on the managed content or the semantic search functionality provided is limited as it depends on the manual annotation of content by users. So, in this study we describe a semantic content management flow by extracting implicit knowledge from both the structure of the CMSes and actual content within them. The task of additional semantic knowledge gathering and providing semantic operations on the content is a challenging task which includes adoption of several latest advancements in information extraction (IE), information retrieval (IR) and Semantic Web areas. In this study, we propose a new approach which provides automatic annotation of content managed in CMSes with the information retrieved from the Linked Open Data (LOD) cloud and several semantic operations on the content in terms of storage and search. We use a simple RDF path language to create custom indexes and retrive semantic knowledge from the LOD cloud suitable for specific use cases. All additional knowledge is materialized along with the actual content of document in dedicated indexes. This semantix indexing infrastructure allows semantically meaningful search facilities on top of it. We realize our approach in the scope of Apache Stanbol project, which is a subproject developed in the scope of IKS project, by focusing on document storage and retrieval. We evaluate our approach in healthcare domain with different domain ontologies (SNOMED/CT, ART, RXNORM) in addition to DBpedia as parts of LOD cloud which are used to annotate documents and content obtained from different health portals.

Optimization of Just-in-Time Adaptive Interventions Using Reinforcement Learning.

Momentary context data is an important source for intelligent decision making towards personalization of mobile phone notifications. We propose a reinforcement learning based personalized notification delivery algorithm, reasoning over momentary context data. Beyond the state of the art, we propose new approaches for faster convergence of the algorithm and jump start of learning performance at the beginning of the learning process. We test our approach in both simulated and real settings trying to optimize the timing of the notifications. Our eventual, practical aim is to make office workers more physically active during the work time. We compare the results obtained for standard and improved algorithms in both testbeds where improved versions yield better results.

An Interoperability Platform Enabling Reuse of Electronic Health Records for Signal Verification Studies.

Depending mostly on voluntarily sent spontaneous reports, pharmacovigilance studies are hampered by low quantity and quality of patient data. Our objective is to improve postmarket safety studies by enabling safety analysts to seamlessly access a wide range of EHR sources for collecting deidentified medical data sets of selected patient populations and tracing the reported incidents back to original EHRs. We have developed an ontological framework where EHR sources and target clinical research systems can continue using their own local data models, interfaces, and terminology systems, while structural interoperability and Semantic Interoperability are handled through rule-based reasoning on formal representations of different models and terminology systems maintained in the SALUS Semantic Resource Set. SALUS Common Information Model at the core of this set acts as the common mediator. We demonstrate the capabilities of our framework through one of the SALUS safety analysis tools, namely, the Case Series Characterization Tool, which have been deployed on top of regional EHR Data Warehouse of the Lombardy Region containing about 1 billion records from 16 million patients and validated by several pharmacovigilance researchers with real-life cases. The results confirm significant improvements in signal detection and evaluation compared to traditional methods with the missing background information.

Semantic Content Management with Apache Stanbol

Most of the CMS platforms lack the management of semantic information about the content although a lot of research has been carried out. The IKS project has introduced a reference architecture for Semantic Content Management Systems (SCMS). The objective is to merge the latest advancements in semantic web technologies with the needs of legacy CMS platforms. Apache Stanbol is a part of this SCMS reference implementation.