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Abstract
Introduction Ontology-based annotation of evidence, using disease-specific ontologies, can accelerate analysis and interpretation of the knowledge domain of diseases. Although many domain-specific disease ontologies have been developed so far, in the area of cardiovascular diseases, there is a lack of ontological representation of the disease knowledge domain of stroke. Methods The stroke ontology (STO) was created on the basis of the ontology development life cycle and was built using Protégé ontology editor in the ontology web language format. The ontology was evaluated in terms of structural and functional features, expert evaluation, and competency questions. Results The stroke ontology covers a broad range of major biomedical and risk factor concepts. The majority of concepts are enriched by synonyms, definitions, and references. The ontology attempts to incorporate different users’ views on the stroke domain such as neuroscientists, molecular biologists, and clinicians. Evaluation of the ontology based on natural language processing showed a high precision (0.94), recall (0.80), and F-score (0.78) values, indicating that STO has an acceptable coverage of the stroke knowledge domain. Performance evaluation using competency questions designed by a clinician showed that the ontology can be used to answer expert questions in light of published evidence. Conclusions The stroke ontology is the first, multiple-view ontology in the domain of brain stroke that can be used as a tool for representation, formalization, and standardization of the heterogeneous data related to the stroke domain. Since this is a draft version of the ontology, the contribution of the stroke scientific community can help to improve the usability of the current version.
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Machado CM, Rebholz-Schuhmann D, Freitas AT, Couto FM. The semantic web in translational medicine: current applications and future directions. Brief Bioinform 2015; 16:89-103. [PMID: 24197933 PMCID: PMC4293377 DOI: 10.1093/bib/bbt079] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2013] [Accepted: 10/08/2013] [Indexed: 11/14/2022] Open
Abstract
Semantic web technologies offer an approach to data integration and sharing, even for resources developed independently or broadly distributed across the web. This approach is particularly suitable for scientific domains that profit from large amounts of data that reside in the public domain and that have to be exploited in combination. Translational medicine is such a domain, which in addition has to integrate private data from the clinical domain with proprietary data from the pharmaceutical domain. In this survey, we present the results of our analysis of translational medicine solutions that follow a semantic web approach. We assessed these solutions in terms of their target medical use case; the resources covered to achieve their objectives; and their use of existing semantic web resources for the purposes of data sharing, data interoperability and knowledge discovery. The semantic web technologies seem to fulfill their role in facilitating the integration and exploration of data from disparate sources, but it is also clear that simply using them is not enough. It is fundamental to reuse resources, to define mappings between resources, to share data and knowledge. All these aspects allow the instantiation of translational medicine at the semantic web-scale, thus resulting in a network of solutions that can share resources for a faster transfer of new scientific results into the clinical practice. The envisioned network of translational medicine solutions is on its way, but it still requires resolving the challenges of sharing protected data and of integrating semantic-driven technologies into the clinical practice.
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Affiliation(s)
- Catia M. Machado
- *Corresponding author. Catia M. Machado, Departamento de Informática, Faculdade de Ciências, Universidade de Lisboa, Portugal and Instituto de Engenharia de Sistemas e Computadores - Investigação e Desenvolvimento, Universidade de Lisboa, Portugal. E-mail:
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Liaw ST, Taggart J, Yu H, de Lusignan S, Kuziemsky C, Hayen A. Integrating electronic health record information to support integrated care: practical application of ontologies to improve the accuracy of diabetes disease registers. J Biomed Inform 2014; 52:364-72. [PMID: 25089026 DOI: 10.1016/j.jbi.2014.07.016] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2014] [Revised: 07/21/2014] [Accepted: 07/23/2014] [Indexed: 11/28/2022]
Abstract
BACKGROUND Information in Electronic Health Records (EHRs) are being promoted for use in clinical decision support, patient registers, measurement and improvement of integration and quality of care, and translational research. To do this EHR-derived data product creators need to logically integrate patient data with information and knowledge from diverse sources and contexts. OBJECTIVE To examine the accuracy of an ontological multi-attribute approach to create a Type 2 Diabetes Mellitus (T2DM) register to support integrated care. METHODS Guided by Australian best practice guidelines, the T2DM diagnosis and management ontology was conceptualized, contextualized and validated by clinicians; it was then specified, formalized and implemented. The algorithm was standardized against the domain ontology in SNOMED CT-AU. Accuracy of the implementation was measured in 4 datasets of varying sizes (927-12,057 patients) and an integrated dataset (23,793 patients). Results were cross-checked with sensitivity and specificity calculated with 95% confidence intervals. RESULTS Incrementally integrating Reason for Visit (RFV), medication (Rx), and pathology in the algorithm identified nearly100% of T2DM cases. Incrementally integrating the four datasets improved accuracy; controlling for sample size, data incompleteness and duplicates. Manual validation confirmed the accuracy of the algorithm. CONCLUSION Integrating multiple data elements within an EHR using ontology-based case-finding algorithms can improve the accuracy of the diagnosis and compensate for suboptimal data quality, and hence creating a dataset that is more fit-for-purpose. This clinical and pragmatic application of ontologies to EHR data improves the integration of data and the potential for better use of data to improve the quality of care.
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Affiliation(s)
- Siaw-Teng Liaw
- School of Public Health and Community Medicine, UNSW Medicine, Sydney, Australia; Centre for PHC & Equity, UNSW Medicine, Sydney, Australia; Academic General Practice Unit, South Western Sydney Local Health District, NSW, Australia.
| | - Jane Taggart
- Centre for PHC & Equity, UNSW Medicine, Sydney, Australia
| | - Hairong Yu
- Centre for PHC & Equity, UNSW Medicine, Sydney, Australia
| | | | - Craig Kuziemsky
- Telfer School of Management, University of Ottawa, Ottawa, Canada
| | - Andrew Hayen
- School of Public Health and Community Medicine, UNSW Medicine, Sydney, Australia
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Rahimi A, Parameswaran N, Ray PK, Taggart J, Yu H, Liaw ST. Development of a Methodological Approach for Data Quality Ontology in Diabetes Management. INTERNATIONAL JOURNAL OF E-HEALTH AND MEDICAL COMMUNICATIONS 2014. [DOI: 10.4018/ijehmc.2014070105] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The role of ontologies in chronic disease management and associated challenges such as defining data quality (DQ) and its specification is a current topic of interest. In domains such as Diabetes Management, a robust Data Quality Ontology (DQO) is required to support the automation of data extraction semantically from Electronic Health Record (EHR) and access and manage DQ, so that the data set is fit for purpose. A five steps strategy is proposed in this paper to create the DQO which captures the semantics of clinical data. It consists of: (1) Knowledge acquisition; (2) Conceptualization; (3) Semantic modeling; (4) Knowledge representation; and (5) Validation. The DQO was applied to the identification of patients with Type 2 Diabetes Mellitus (T2DM) in EHRs, which included an assessment of the DQ of the EHR. The five steps methodology is generalizable and reusable in other domains.
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Affiliation(s)
- Alireza Rahimi
- UNSW School of Public Health and Community Medicine, Sydney, Australia & Isfahan University of Medical Sciences, Health information Technology Research Centre, Iran & UNSW Asia-Pacific ubiquitous Healthcare Research Centre, Sydney, Australia & SWSLHD General Practice Unit, Sydney, Australia
| | - Nandan Parameswaran
- UNSW, School of Computer Science and Engineering, Sydney, Australia & UNSW Asia-Pacific ubiquitous Healthcare Research Centre, Sydney, Australia
| | - Pradeep Kumar Ray
- UNSW, Asia-Pacific Ubiquitous Healthcare Research Centre, Sydney, Australia & UNSW, Australian School of Business, Sydney, Australia
| | - Jane Taggart
- UNSW, Centre for Primary Health Care & Equity, Sydney, Australia & SWSLHD General Practice Unit, Fairfield, Sydney, Australia
| | - Hairong Yu
- UNSW, Centre for Primary Health Care and Equity, Sydney, Australia
| | - Siaw-Teng Liaw
- UNSW, School of Public Health and Community Medicine, Sydney & UNSW, Centre for Primary Health Care and Equity, Sydney, Australia & UNSW, Asia-Pacific Ubiquitous Healthcare Research Centre, Sydney, Australia & SWSLHD General Practice Unit, Sydney, Australia
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Rahimi A, Liaw ST, Taggart J, Ray P, Yu H. Validating an ontology-based algorithm to identify patients with type 2 diabetes mellitus in electronic health records. Int J Med Inform 2014; 83:768-78. [PMID: 25011429 DOI: 10.1016/j.ijmedinf.2014.06.002] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2014] [Revised: 06/02/2014] [Accepted: 06/02/2014] [Indexed: 11/19/2022]
Abstract
BACKGROUND Improving healthcare for people with chronic conditions requires clinical information systems that support integrated care and information exchange, emphasizing a semantic approach to support multiple and disparate Electronic Health Records (EHRs). Using a literature review, the Australian National Guidelines for Type 2 Diabetes Mellitus (T2DM), SNOMED-CT-AU and input from health professionals, we developed a Diabetes Mellitus Ontology (DMO) to diagnose and manage patients with diabetes. This paper describes the manual validation of the DMO-based approach using real world EHR data from a general practice (n=908 active patients) participating in the electronic Practice Based Research Network (ePBRN). METHOD The DMO-based algorithm to query, using Semantic Protocol and RDF Query Language (SPARQL), the structured fields in the ePBRN data repository were iteratively tested and refined. The accuracy of the final DMO-based algorithm was validated with a manual audit of the general practice EHR. Contingency tables were prepared and Sensitivity and Specificity (accuracy) of the algorithm to diagnose T2DM measured, using the T2DM cases found by manual EHR audit as the gold standard. Accuracy was determined with three attributes - reason for visit (RFV), medication (Rx) and pathology (path) - singly and in combination. RESULTS The Sensitivity and Specificity of the algorithm were 100% and 99.88% with RFV; 96.55% and 98.97% with Rx; and 15.6% and 98.92% with Path. This suggests that Rx and Path data were not as complete or correct as the RFV for this general practice, which kept its RFV information complete and current for diabetes. However, the completeness is good enough for this purpose as confirmed by the very small relative deterioration of the accuracy (Sensitivity and Specificity of 97.67% and 99.18%) when calculated for the combination of RFV, Rx and Path. The manual EHR audit suggested that the accuracy of the algorithm was influenced by data quality such as incorrect data due to mistaken units of measurement and unavailable data due to non-documentation or documented in the wrong place or progress notes, problems with data extraction, encryption and data management errors. CONCLUSION This DMO-based algorithm is sufficiently accurate to support a semantic approach, using the RFV, Rx and Path to define patients with T2DM from EHR data. However, the accuracy can be compromised by incomplete or incorrect data. The extent of compromise requires further study, using ontology-based and other approaches.
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Affiliation(s)
- Alireza Rahimi
- UNSW, School of Public Health & Community Medicine, Sydney, Australia; Isfahan University of Medical Sciences, Health Information Research Centre, Isfahan, Iran; UNSW, Asia-Pacific Ubiquitous Healthcare Research Centre, Sydney, Australia
| | - Siaw-Teng Liaw
- UNSW, School of Public Health & Community Medicine, Sydney, Australia; UNSW, Centre for Primary Health Care & Equity, Sydney, Australia; General Practice Unit, South Western Sydney Local Health District.
| | - Jane Taggart
- UNSW, Centre for Primary Health Care & Equity, Sydney, Australia
| | - Pradeep Ray
- UNSW, Asia-Pacific Ubiquitous Healthcare Research Centre, Sydney, Australia
| | - Hairong Yu
- UNSW, Centre for Primary Health Care & Equity, Sydney, Australia
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Liaw ST, Rahimi A, Ray P, Taggart J, Dennis S, de Lusignan S, Jalaludin B, Yeo AET, Talaei-Khoei A. Towards an ontology for data quality in integrated chronic disease management: a realist review of the literature. Int J Med Inform 2012; 82:10-24. [PMID: 23122633 DOI: 10.1016/j.ijmedinf.2012.10.001] [Citation(s) in RCA: 69] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2012] [Revised: 10/03/2012] [Accepted: 10/05/2012] [Indexed: 11/25/2022]
Abstract
PURPOSE Effective use of routine data to support integrated chronic disease management (CDM) and population health is dependent on underlying data quality (DQ) and, for cross system use of data, semantic interoperability. An ontological approach to DQ is a potential solution but research in this area is limited and fragmented. OBJECTIVE Identify mechanisms, including ontologies, to manage DQ in integrated CDM and whether improved DQ will better measure health outcomes. METHODS A realist review of English language studies (January 2001-March 2011) which addressed data quality, used ontology-based approaches and is relevant to CDM. RESULTS We screened 245 papers, excluded 26 duplicates, 135 on abstract review and 31 on full-text review; leaving 61 papers for critical appraisal. Of the 33 papers that examined ontologies in chronic disease management, 13 defined data quality and 15 used ontologies for DQ. Most saw DQ as a multidimensional construct, the most used dimensions being completeness, accuracy, correctness, consistency and timeliness. The majority of studies reported tool design and development (80%), implementation (23%), and descriptive evaluations (15%). Ontological approaches were used to address semantic interoperability, decision support, flexibility of information management and integration/linkage, and complexity of information models. CONCLUSION DQ lacks a consensus conceptual framework and definition. DQ and ontological research is relatively immature with little rigorous evaluation studies published. Ontology-based applications could support automated processes to address DQ and semantic interoperability in repositories of routinely collected data to deliver integrated CDM. We advocate moving to ontology-based design of information systems to enable more reliable use of routine data to measure health mechanisms and impacts.
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Affiliation(s)
- S T Liaw
- University of NSW School of Public Health & Community Medicine, Sydney, Australia.
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