Arash Shaban-Nejad

Semantic querying of relational data for clinical intelligence: a semantic web services-based approach

BackgroundClinical Intelligence, as a research and engineering discipline, is dedicated to the development of tools for data analysis for the purposes of clinical research, surveillance, and effective health care management. Self-service ad hoc querying of clinical data is one desirable type of functionality. Since most of the data are currently stored in relational or similar form, ad hoc querying is problematic as it requires specialised technical skills and the knowledge of particular data schemas.ResultsA possible solution is semantic querying where the user formulates queries in terms of domain ontologies that are much easier to navigate and comprehend than data schemas. In this article, we are exploring the possibility of using SADI Semantic Web services for semantic querying of clinical data. We have developed a prototype of a semantic querying infrastructure for the surveillance of, and research on, hospital-acquired infections.ConclusionsOur results suggest that SADI can support ad-hoc, self-service, semantic queries of relational data in a Clinical Intelligence context. The use of SADI compares favourably with approaches based on declarative semantic mappings from data schemas to ontologies, such as query rewriting and RDFizing by materialisation, because it can easily cope with situations when (i) some computation is required to turn relational data into RDF or OWL, e.g., to implement temporal reasoning, or (ii) integration with external data sources is necessary.

An infrastructure for real-time population health assessment and monitoring

The fragmented nature of population health information is a barrier to public health practice. Despite repeated demands by policymakers, administrators, and practitioners to develop information systems that provide a coherent view of population health status, there has been limited progress toward developing such an infrastructure. We are creating an informatics platform for describing and monitoring the health status of a defined population by integrating multiple clinical and administrative data sources. This infrastructure, which involves a population health record, is designed to enable development of detailed portraits of population health, facilitate monitoring of population health indicators, enable evaluation of interventions, and provide clinicians and patients with population context to assist diagnostic and therapeutic decision-making. In addition to supporting public health professionals, clinicians, and the public, we are designing the infrastructure to provide a platform for public health informatics research. This early report presents the requirements and architecture for the infrastructure and describes the initial implementation of the population health record, focusing on indicators of chronic diseases related to obesity.

Towards clinical intelligence with SADI semantic web services: a case study with hospital-acquired infections data

Clinical Intelligence, as a research and engineering discipline, is dedicated to the development of tools for data analysis for the purposes of clinical research, surveillance and rational health care management. Ad hoc querying of clinical data is one desirable type of functionality. Since most of the data is currently stored in relational or similar form, ad hoc querying is problematic as it requires specialised technical skills and the knowledge of particular data schemas. A possible solution is semantic querying where the user formulates queries in terms of domain ontologies that are much easier to navigate and comprehend than data schemas. Existing approaches to semantic querying of relational data, based on declarative semantic mappings from data schemas to ontologies, such as RDFizing and query rewriting, cannot cope with situations when some computation is required to turn relational data into RDF or OWL, e. g., to implement temporal reasoning. In this paper, we are exploring the possibility of using SADI Semantic Web services for semantic querying of clinical data and report preliminary progress on prototyping a semantic querying infrastructure for the surveillance of, and research on hospital-acquired infections.

COPE: childhood obesity prevention [knowledge] enterprise

This paper presents our work-in-progress on designing and implementing an integrated ontology for widespread knowledge dissemination in the domain of obesity with emphasis on childhood obesity. The COPE ontology aims to support a knowledge-based infrastructure to promote healthy eating habits and lifestyles, analyze childrens behaviors and habits associated with obesity and to prevent or reduce the prevalence of childhood obesity and overweight. By formally integrating and harmonizing multiple knowledge sources across disciplinary boundaries, we will facilitate cross-sectional analysis of the domain of obesity and generate both generic and customized preventive recommendations, which take into consideration several factors, including existing conditions in individuals and communities.

An Innovative Approach to Addressing Childhood Obesity: A Knowledge-Based Infrastructure for Supporting Multi-Stakeholder Partnership Decision-Making in Quebec, Canada

Multi-stakeholder partnerships (MSPs) have become a widespread means for deploying policies in a whole of society strategy to address the complex problem of childhood obesity. However, decision-making in MSPs is fraught with challenges, as decision-makers are faced with complexity, and have to reconcile disparate conceptualizations of knowledge across multiple sectors with diverse sets of indicators and data. These challenges can be addressed by supporting MSPs with innovative tools for obtaining, organizing and using data to inform decision-making. The purpose of this paper is to describe and analyze the development of a knowledge-based infrastructure to support MSP decision-making processes. The paper emerged from a study to define specifications for a knowledge-based infrastructure to provide decision support for community-level MSPs in the Canadian province of Quebec. As part of the study, a process assessment was conducted to understand the needs of communities as they collect, organize, and analyze data to make decisions about their priorities. The result of this process is a “portrait”, which is an epidemiological profile of health and nutrition in their community. Portraits inform strategic planning and development of interventions, and are used to assess the impact of interventions. Our key findings indicate ambiguities and disagreement among MSP decision-makers regarding causal relationships between actions and outcomes, and the relevant data needed for making decisions. MSP decision-makers expressed a desire for easy-to-use tools that facilitate the collection, organization, synthesis, and analysis of data, to enable decision-making in a timely manner. Findings inform conceptual modeling and ontological analysis to capture the domain knowledge and specify relationships between actions and outcomes. This modeling and analysis provide the foundation for an ontology, encoded using OWL 2 Web Ontology Language. The ontology is developed to provide semantic support for the MSP process, defining objectives, strategies, actions, indicators, and data sources. In the future, software interacting with the ontology can facilitate interactive browsing by decision-makers in the MSP in the form of concepts, instances, relationships, and axioms. Our ontology also facilitates the integration and interpretation of community data, and can help in managing semantic interoperability between different knowledge sources. Future work will focus on defining specifications for the development of a database of indicators and an information system to help decision-makers to view, analyze and organize indicators for their community. This work should improve MSP decision-making in the development of interventions to address childhood obesity.

Vaccine attitude surveillance using semantic analysis: constructing a semantically annotated corpus

This paper reports work in progress to semantically annotate blog posts about vaccines to use in the Vaccine Attitude Surveillance using Semantic Analysis (VASSA) framework. The VASSA framework combines semantic web and natural language processing (NLP) tools and techniques to provide a coherent semantic layer across online social media for assessment and analysis of vaccination attitudes and beliefs. We describe how the blog posts were sampled and selected, our schema to semantically annotate concepts defined in our ontology, details of the annotation process, and inter-annotator agreement on a sample of blog posts.

An OWL 2-Based Knowledge Platform Combining the Social and Semantic Webs for an Ambient Childhood Obesity Prevention System

Amid the extremely active Semantic Web community and the Social Webs exceptionally rising popularity, experts believe that an amplified fusion between the two webs will give rise to the next huge advancement in Web intelligence. Such advances can particularly be translated into ambient and ubiquitous systems and applications. In this paper, we delve into the recent advances in knowledge representation, semantic web, natural language processing and online social networking data and concepts, to propose an inclusive platform and framework defining ambient recommender and decision support systems that aim at facilitating cross-sectional analysis of the domain of childhood obesity and generating both generic and customized preventive recommendations.

Knowledge-based surveillance for preventing postoperative surgical site infection.

At least one out of every twenty people admitted to a Canadian hospital will acquire an infection. These hospital-acquired infections (HAIs) take a profound individual and system-wide toll, resulting in thousands of deaths and hundreds of millions of dollars in additional expenses each year. Surveillance for HAIs is essential to develop and evaluate prevention and control efforts. In nearly all healthcare institutions, however, surveillance for HAIs is a manual process, requiring highly trained infection control practitioners to consult multiple information systems and paper charts. The amount of effort required for discovery and integration of relevant data from multiple sources limits the current effectiveness of HAIs surveillance. In this research, we apply knowledge modeling and semantic technologies to facilitate the integration of disparate data and enable automatic reasoning with these integrated data to identify events of clinical interest. In this paper, we focus on Surgical Site Infections (SSIs), which account for a relatively large fraction of all hospital acquired infections.

PopHR: a knowledge‐based platform to support integration, analysis, and visualization of population health data

Population health decision makers must consider complex relationships between multiple concepts measured with differential accuracy from heterogeneous data sources. Population health information systems are currently limited in their ability to integrate data and present a coherent portrait of population health. Consequentially, these systems can provide only basic support for decision makers. The Population Health Record (PopHR) is a semantic web application that automates the integration and extraction of massive amounts of heterogeneous data from multiple distributed sources (e.g., administrative data, clinical records, and survey responses) to support the measurement and monitoring of population health and health system performance for a defined population. The design of the PopHR draws on the theories of the determinants of health and evidence‐based public health to harmonize and explicitly link information about a population with evidence about the epidemiology and control of chronic diseases. Organizing information in this manner and linking it explicitly to evidence is expected to improve decision making related to the planning, implementation, and evaluation of population health and health system interventions. In this paper, we describe the PopHR platform and discuss the architecture, design, key modules, and its implementation and use.

An enhanced graph-oriented approach for change management in distributed biomedical ontologies and linked data

This paper reports the summary and results of our research on providing a graph oriented formalism to represent, analyze and validate the evolution of bio-ontologies, with emphasis on the FungalWeb Ontology. In this approach Category theory along with rule-based hierarchical distributed (HD) graph transformation have been employed to propose a more specific semantics for analyzing ontological changes and transformations between different versions of an ontology, as well as tracking the effects of a change in different levels of abstractions.