SemanticDB: A Semantic Web Infrastructure for Clinical Research and Quality Reporting

Pierce, Christopher; Booth, David E.; Ogbuji, Chimezie; Deaton, Chris; Blackstone, Eugene H.; Lenat, Douglas B.

doi:10.2174/157489312802460730

Cited by 14 publications

(6 citation statements)

References 3 publications

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“…The Cleveland clinic supported a project called Semantic-DB [ 29 ] that proposed a framework to collect, store, and reuse knowledge to support sufficiency, flexibility, and extensibility of different clinical data. The reliability of research results and the accuracy of quality metrics are the addressed issues in this paper.…”

Section: Resultsmentioning

confidence: 99%

Issues Associated With the Use of Semantic Web Technology in Knowledge Acquisition for Clinical Decision Support Systems: Systematic Review of the Literature

Zolhavarieh¹,

Parry²,

Bai³

2017

JMIR Med Inform

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BackgroundKnowledge-based clinical decision support system (KB-CDSS) can be used to help practitioners make diagnostic decisions. KB-CDSS may use clinical knowledge obtained from a wide variety of sources to make decisions. However, knowledge acquisition is one of the well-known bottlenecks in KB-CDSSs, partly because of the enormous growth in health-related knowledge available and the difficulty in assessing the quality of this knowledge as well as identifying the “best” knowledge to use. This bottleneck not only means that lower-quality knowledge is being used, but also that KB-CDSSs are difficult to develop for areas where expert knowledge may be limited or unavailable. Recent methods have been developed by utilizing Semantic Web (SW) technologies in order to automatically discover relevant knowledge from knowledge sources.ObjectiveThe two main objectives of this study were to (1) identify and categorize knowledge acquisition issues that have been addressed through using SW technologies and (2) highlight the role of SW for acquiring knowledge used in the KB-CDSS.MethodsWe conducted a systematic review of the recent work related to knowledge acquisition MeM for clinical decision support systems published in scientific journals. In this regard, we used the keyword search technique to extract relevant papers.ResultsThe retrieved papers were categorized based on two main issues: (1) format and data heterogeneity and (2) lack of semantic analysis. Most existing approaches will be discussed under these categories. A total of 27 papers were reviewed in this study.ConclusionsThe potential for using SW technology in KB-CDSS has only been considered to a minor extent so far despite its promise. This review identifies some questions and issues regarding use of SW technology for extracting relevant knowledge for a KB-CDSS.

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Section: Resultsmentioning

confidence: 99%

Issues Associated With the Use of Semantic Web Technology in Knowledge Acquisition for Clinical Decision Support Systems: Systematic Review of the Literature

Zolhavarieh¹,

Parry²,

Bai³

2017

JMIR Med Inform

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“…In empirical evaluations, the following datasets were used: a subset of DBpedia [11] and a subset of SemanticDB [12]. SemanticDB is a Semantic Web data repository developed by the Cleveland Clinic for Clinical Research and Quality Reporting.…”

Section: Discussionmentioning

confidence: 99%

A Suggestion-Based RDF Instance Matching System

Aydar¹,

Ayvaz²

2017

IJCTE

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This paper presents a semi-automatic recommendation-based instance matching system using RDF graph data. Based on a graph node similarity algorithm, our instance matching system detects instance nodes with similarities higher than an input threshold value and returns to the user the subject node pairs. The system merges a matched node pair when the user confirms the matched nodes in the results. After a merge, the merged node is also considered as an entity for the following candidate pair generation cycle. The procedure continues until no new matching candidate pairs are recommended by the algorithm and there is no more feedback provided by the user.

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“…(15,18,39) "OWL reasoning," another capability afforded by RDF, is an extension of this knowledge level, in that new assertions can be concluded based on existing assertions in the triplestore. (21,40,41) For this project specifically, we used a small subset of VANDF to query medications at the knowledge level to enable easier querying (e.g., querying by rolled-up categories rather than hundreds of individual medication codes). With this success in mind, we believe that linking with ontologies and leveraging OWL reasoning could potentially reveal new hypotheses to explore, via patterns in the data that may be hidden at the individual medication code level.…”

Section: Future Workmentioning

confidence: 99%

Cohort Identification Using Semantic Web Technologies: Triplestores as Engines for Complex Computable Phenotyping

Pfaff

Bradford

Clark

et al. 2021

Preprint

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BackgroundComputable phenotypes are increasingly important tools for patient cohort identification. As part of a study of risk of chronic opioid use after surgery, we used a Resource Description Framework (RDF) triplestore as our computable phenotyping platform, hypothesizing that the unique affordances of triplestores may aid in making complex computable phenotypes more interoperable and reproducible than traditional relational database queries.To identify and model risk for new chronic opioid users post-surgery, we loaded several heterogeneous data sources into a Blazegraph triplestore: (1) electronic health record data; (2) claims data; (3) American Community Survey data; and (4) Centers for Disease Control Social Vulnerability Index, opioid prescription rate, and drug poisoning rate data. We then ran a series of queries to execute each of the rules in our “new chronic opioid user” phenotype definition to ultimately arrive at our qualifying cohort.ResultsOf the 4,163 patients in the denominator, our computable phenotype identified 248 patients as new chronic opioid users after their index surgical procedure. After validation against charts, 228 of the 248 were revealed to be true positive cases, giving our phenotype a PPV of 0.92.ConclusionWe successfully used the triplestore to execute the new chronic opioid user phenotype logic, and in doing so noted some advantages of the triplestore in terms of schemalessness, interoperability, and reproducibility. Future work will use the triplestore to create the planned risk model and leverage the additional links with ontologies, and ontological reasoning.

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SemanticDB: A Semantic Web Infrastructure for Clinical Research and Quality Reporting

Cited by 14 publications

References 3 publications

Issues Associated With the Use of Semantic Web Technology in Knowledge Acquisition for Clinical Decision Support Systems: Systematic Review of the Literature

Issues Associated With the Use of Semantic Web Technology in Knowledge Acquisition for Clinical Decision Support Systems: Systematic Review of the Literature

A Suggestion-Based RDF Instance Matching System

Cohort Identification Using Semantic Web Technologies: Triplestores as Engines for Complex Computable Phenotyping

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