Epilepsy and seizure ontology: towards an epilepsy informatics infrastructure for clinical research and patient care

Sahoo, Satya S.; Lhatoo, Samden D.; Dk, Gupta; Cui, Licong; Zhao, Meng; Jayapandian, Catherine P.; Bozorgi, Alireza; Zhang, Guo Qiang

doi:10.1136/amiajnl-2013-001696

Cited by 65 publications

(56 citation statements)

References 37 publications

(36 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Epilepsy and Seizure Ontology (EpSO) was developed as a domain ontology to address multiple data management challenges in epilepsy research, including integration of heterogeneous data, querying, and data validation [20]. EpSO is already used in a patient information capture system, which is deployed at the Epilepsy Monitoring Unit (EMU) at University Hospitals of Cleveland, to ensure consistency in collection of patient information [21].…”

Section: Epilepsy and Seizure Ontology: Conceptual Modeling For Signamentioning

confidence: 99%

Domain Ontology As Conceptual Model for Big Data Management: Application in Biomedical Informatics

Jayapandian

Chen

Dabir

et al. 2014

Conceptual Modeling

Self Cite

View full text Add to dashboard Cite

Abstract. The increasing capability and sophistication of biomedical instruments has led to rapid generation of large volumes of disparate data that is often characterized as biomedical "big data". Effective analysis of biomedical big data is providing new insights to advance healthcare research, but it is difficult to efficiently manage big data without a conceptual model, such as ontology, to support storage, query, and analytical functions. In this paper, we describe the Cloudwave platform that uses a domain ontology to support optimal data partitioning, efficient network transfer, visualization, and querying of big data in the neurology disease domain. The domain ontology is used to define a new JSONbased Cloudwave Signal Format (CSF) for neurology signal data. A comparative evaluation of the ontology-based CSF with existing data format demonstrates that it significantly reduces the data access time for query and visualization of large scale signal data.

show abstract

Section: Epilepsy and Seizure Ontology: Conceptual Modeling For Signamentioning

confidence: 99%

Domain Ontology As Conceptual Model for Big Data Management: Application in Biomedical Informatics

Jayapandian

Chen

Dabir

et al. 2014

Conceptual Modeling

Self Cite

View full text Add to dashboard Cite

show abstract

“…To address this and similar data heterogeneity challenges, the Ontology Layer of Insight used a list of “common data elements” (CDE), which was collaboratively developed to represent the study data. The CDEs used terms defined in existing terminological systems, such as the Institute of Medicine's Report on “Epilepsy Across the Spectrum: Promoting Health and Understanding 2012,” the recommended standards for epilepsy surveillance studies, the National Institute of Neurological Disorders and Stroke (NINDS) Common Data Elements (CDE), and the Behavioral Risk Factor Surveillance System (BRFSS) Questionnaire [12]. The MEW Network CDEs were modeled in an ontology by extending an existing domain ontology for epilepsy research called the Epilepsy and Seizure Ontology (EpSO) [12].…”

Section: Integrating and Analyzing Healthcare Data In The Mew Netmentioning

confidence: 99%

“…EpSO 1 models terms described by the “four-dimensional classification” approach, including seizures (representing abnormal electrical activity in the brain), the location of seizures, the cause of the epilepsy, and related medical conditions [12]. It re-uses ontology concepts defined in the Foundational Model of Anatomy (FMA) [16] for modeling detailed brain anatomy, drug information (brand names and drug ingredients) from the RxNorm (developed at the US National Library of Medicine (NLM) [17], and evoked potentials from the Neural Electromagnetic Ontologies (NEMO) [18].…”

Section: Integrating and Analyzing Healthcare Data In The Mew Netmentioning

confidence: 99%

“…There are only a few clinical research systems that use Semantic Web technologies for data management, for example the Informatics for Integrating Biology and the Bedside (I2B2) is a large-scale initiative to integrate and analyze electronic health records (EHR) for clinical research [12]. The I2B2 uses a traditional data warehouse approach with Extract Transform Load (ETL) process to integrate EHR data and uses multiple ontologies, such as components of the International Classification of Diseases (ICD-9).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Insight: Semantic provenance and analysis platform for multi-center neurology healthcare research

Ramesh

Wei

Welter

et al. 2015

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

View full text Add to dashboard Cite

Insight is a Semantic Web technology-based platform to support large-scale secondary analysis of healthcare data for neurology clinical research. Insight features the novel use of: (1) provenance metadata, which describes the history or origin of patient data, in clinical research analysis, and (2) support for patient cohort queries across multiple institutions conducting research in epilepsy, which is the one of the most common neurological disorders affecting 50 million persons worldwide. Insight is being developed as a healthcare informatics infrastructure to support a national network of eight epilepsy research centers across the U.S. funded by the U.S. Centers for Disease Control and Prevention (CDC). This paper describes the use of the World Wide Web Consortium (W3C) PROV recommendation for provenance metadata that allows researchers to create patient cohorts based on the provenance of the research studies. In addition, the paper describes the use of descriptive logic-based OWL2 epilepsy ontology for cohort queries with “expansion of query expression” using ontology reasoning. Finally, the evaluation results for the data integration and query performance are described using data from three research studies with 180 epilepsy patients. The experiment results demonstrate that Insight is a scalable approach to use Semantic provenance metadata for context-based data analysis in healthcare informatics.

show abstract

“…The four primary goals of Insight are: (1) integrate data using MEW CDEs as standard terminology, (2) enable users to interactively explore the data using visualization functions, (3) provide an intuitive and flexible provenance-enabled query environment for users to perform cohort identification; and (4) serve as the basis of a proposed epilepsy self-management registry. Insight uses an epilepsy domain ontology called Epilepsy and Seizure Ontology (EpSO) (19) to support advanced data exploration, query composition, and execution strategies through use of ontology reasoning for “query unfolding” and for reconciling semantic heterogeneity. These techniques are described in detail in the next section.…”

Section: Introductionmentioning

confidence: 99%

Insight : An ontology-based integrated database and analysis platform for epilepsy self-management research

Sahoo

Ramesh

Welter

et al. 2016

International Journal of Medical Informatics

View full text Add to dashboard Cite

We present Insight as an integrated database and analysis platform for epilepsy self-management research as part of the national Managing Epilepsy Well Network. Insight is the only available informatics platform for accessing and analyzing integrated data from multiple epilepsy self-management research studies with several new data management features and user-friendly functionalities. The features of Insight include, (1) use of Common Data Elements defined by members of the research community and an epilepsy domain ontology for data integration and querying, (2) visualization tools to support real time exploration of data distribution across research studies, and (3) an interactive visual query interface for provenance-enabled research cohort identification. The Insight platform contains data from five completed epilepsy self-management research studies covering various categories of data, including depression, quality of life, seizure frequency, and socioeconomic information. The data represents over 400 epilepsy patients with 7,552 data points. The Insight data exploration and cohort identification query interface has been developed using Ruby on Rails Web technology and open source Web Ontology Language Application Programming Interface to support ontology-based reasoning. We have developed an efficient ontology management module that automatically updates the ontology mappings each time a new version of the Epilepsy and Seizure Ontology is released. The Insight platform features a Role-based Access Control module to authenticate and effectively manage user access to different research studies. User access to Insight is managed by the Managing Epilepsy Well Network database steering committee consisting of representatives of all current collaborating centers of the Managing Epilepsy Well Network. New research studies are being continuously added to the Insight database and the size as well as the unique coverage of the dataset allows investigators to conduct aggregate data analysis that will inform the next generation of epilepsy self-management studies.

show abstract

Epilepsy and seizure ontology: towards an epilepsy informatics infrastructure for clinical research and patient care

Abstract: EpSO plays a critical role in informatics tools for epilepsy patient care and multi-center clinical research.

Cited by 65 publications

References 37 publications

Domain Ontology As Conceptual Model for Big Data Management: Application in Biomedical Informatics

Domain Ontology As Conceptual Model for Big Data Management: Application in Biomedical Informatics

Insight: Semantic provenance and analysis platform for multi-center neurology healthcare research

Insight : An ontology-based integrated database and analysis platform for epilepsy self-management research

Contact Info

Product

Resources

About