Harmonizing semantic annotations for computational models in biology.

Life science researchers use computational models to articulate and test hypotheses about the behavior of biological systems. Semantic annotation is a critical component for enhancing the interoperability and reusability of such models as well as for the integration of the data needed for model parameterization and validation. Encoded as machine-readable links to knowledge resource terms, semantic annotations describe the computational or biological meaning of what models and data represent. These annotations help researchers find and repurpose models, accelerate model composition and enable knowledge integration across model repositories and experimental data stores. However, realizing the potential benefits of semantic annotation requires the development of model annotation standards that adhere to a community-based annotation protocol. Without such standards, tool developers must account for a variety of annotation formats and approaches, a situation that can become prohibitively cumbersome and which can defeat the purpose of linking model elements to controlled knowledge resource terms. Currently, no consensus protocol for semantic annotation exists among the larger biological modeling community. Here, we report on the landscape of current annotation practices among the COmputational Modeling in BIology NEtwork community and provide a set of recommendations for building a consensus approach to semantic annotation.

Model annotation and discovery with the Physiome Model Repository.

BACKGROUND: Mathematics and Phy sics-based simulation models have the potential to help interpret and encapsulate biological phenomena in a computable and reproducible form. Similarly, comprehensive descriptions of such models help to ensure that such models are accessible, discoverable, and reusable. To this end, researchers have developed tools and standards to encode mathematical models of biological systems enabling reproducibility and reuse, tools and guidelines to facilitate semantic description of mathematical models, and repositories in which to archive, share, and discover models. Scientists can leverage these resources to investigate specific questions and hypotheses in a more efficient manner. RESULTS: We have comprehensively annotated a cohort of models with biological semantics. These annotated models are freely available in the Physiome Model Repository (PMR). To demonstrate the benefits of this approach, we have developed a web-based tool which enables users to discover models relevant to their work, with a particular focus on epithelial transport. Based on a semantic query, this tool will help users discover relevant models, suggesting similar or alternative models that the user may wish to explore or use. CONCLUSION: The semantic annotation and the web tool we have developed is a new contribution enabling scientists to discover relevant models in the PMR as candidates for reuse in their own scientific endeavours. This approach demonstrates how semantic web technologies and methodologies can contribute to biomedical and clinical research. The source code and links to the web tool are available at https://github.com/dewancse/model-discovery-tool.

Utility of SNOMED CT in automated expansion of clinical terms in discharge summaries: Testing issues of coverage.

OBJECTIVE: This study tests coverage of SNOMED CT as an expansion source in the process of automated expansion of clinical terms found in discharge summaries. Term expansion is commonly used as a technique in knowledge extraction, query formulation and semantic modelling among other applications. However, characteristics of the sources might affect credibility of outputs, and coverage is one of them. METHOD: We developed an automated method for testing coverage of more than one source at a time. We used several methods to clean our corpus of discharge summaries before we extracted text fragments as candidates for clinical concepts. We then used Unified Medical Language System (UMLS) sources and UMLS REST API to filter concepts from the pool of text fragments. Statistical measures like true positive rate and false negative rate were used to decide on the coverage of the source. We also tested the coverage of the individual SNOMED CT hierarchies using the same methods. RESULTS: Findings suggest that a combination of four terminologies tested (SNOMED CT, NCI, LNC and MSH) achieves over 90% of coverage for term expansion. We also found that the SNOMED CT hierarchies that hold clinically relevant concepts provided 60% of coverage. CONCLUSION: We believe that our findings and the method we developed will be of use to both scientists and practitioners working in the domain of knowledge extraction.

NLIMED: Natural Language Interface for Model Entity Discovery in Biosimulation Model Repositories.

Semantic annotation is a crucial step to assure reusability and reproducibility of biosimulation models in biology and physiology. For this purpose, the COmputational Modeling in BIology NEtwork (COMBINE) community recommends the use of the Resource Description Framework (RDF). This grounding in RDF provides the flexibility to enable searching for entities within models (e.g., variables, equations, or entire models) by utilizing the RDF query language SPARQL. However, the rigidity and complexity of the SPARQL syntax and the nature of the tree-like structure of semantic annotations, are challenging for users. Therefore, we propose NLIMED, an interface that converts natural language queries into SPARQL. We use this interface to query and discover model entities from repositories of biosimulation models. NLIMED works with the Physiome Model Repository (PMR) and the BioModels database and potentially other repositories annotated using RDF. Natural language queries are first "chunked" into phrases and annotated against ontology classes and predicates utilizing different natural language processing tools. Then, the ontology classes and predicates are composed as SPARQL and finally ranked using our SPARQL Composer and our indexing system. We demonstrate that NLIMED's approach for chunking and annotating queries is more effective than the NCBO Annotator for identifying relevant ontology classes in natural language queries.Comparison of NLIMED's behavior against historical query records in the PMR shows that it can adapt appropriately to queries associated with well-annotated models.

Model driven development of clinical information sytems using openEHR.

openEHR and the recent international standard (ISO 13606) defined a model driven software development methodology for health information systems. However there is little evidence in the literature describing implementation; especially for desktop clinical applications. This paper presents an implementation pathway using .Net/C# technology for Microsoft Windows desktop platforms. An endoscopy reporting application driven by openEHR Archetypes and Templates has been developed. A set of novel GUI directives has been defined and presented which guides the automatic graphical user interface generator to render widgets properly. We also reveal the development steps and important design decisions; from modelling to the final software product. This might provide guidance for other developers and form evidence required for the adoption of these standards for vendors and national programs alike.

Verifying the Feasibility of Implementing Semantic Interoperability in Different Countries Based on the OpenEHR Approach: Comparative Study of Acute Coronary Syndrome Registries.

BACKGROUND: The semantic interoperability of health care information has been a critical challenge in medical informatics and has influenced the integration, sharing, analysis, and use of medical big data. International standard organizations have developed standards, approaches, and models to improve and implement semantic interoperability. The openEHR approach-one of the standout semantic interoperability approaches-has been implemented worldwide to improve semantic interoperability based on reused archetypes. OBJECTIVE: This study aimed to verify the feasibility of implementing semantic interoperability in different countries by comparing the openEHR-based information models of 2 acute coronary syndrome (ACS) registries from China and New Zealand. METHODS: A semantic archetype comparison method was proposed to determine the semantics reuse degree of reused archetypes in 2 ACS-related clinical registries from 2 countries. This method involved (1) determining the scope of reused archetypes; (2) identifying corresponding data items within corresponding archetypes; (3) comparing the semantics of corresponding data items; and (4) calculating the number of mappings in corresponding data items and analyzing results. RESULTS: Among the related archetypes in the two ACS-related, openEHR-based clinical registries from China and New Zealand, there were 8 pairs of reusable archetypes, which included 89 pairs of corresponding data items and 120 noncorresponding data items. Of the 89 corresponding data item pairs, 87 pairs (98%) were mappable and therefore supported semantic interoperability, and 71 pairs (80%) were labeled as "direct mapping" data items. Of the 120 noncorresponding data items, 114 (95%) data items were generated via archetype evolution, and 6 (5%) data items were generated via archetype localization. CONCLUSIONS: The results of the semantic comparison between the two ACS-related clinical registries prove the feasibility of establishing the semantic interoperability of health care data from different countries based on the openEHR approach. Archetype reuse provides data on the degree to which semantic interoperability exists when using the openEHR approach. Although the openEHR community has effectively promoted archetype reuse and semantic interoperability by providing archetype modeling methods, tools, model repositories, and archetype design patterns, the uncontrolled evolution of archetypes and inconsistent localization have resulted in major challenges for achieving higher levels of semantic interoperability.

Association between Full Electronic Medical Record System Adoption and Drug Use: Antibiotics and Polypharmacy.

OBJECTIVES: We investigated associations between full Electronic Medical Record (EMR) system adoption and drug use in healthcare organizations (HCOs) to explore whether EMR system features such as electronic prescribing, medicines reconciliation, and decision support, might be related to drug use by using the relevant nation-wide data. METHODS: The study design was cross-sectional. Survey data of the level of adoption of EMR systems were collected for the Organization for Economic Co-operation and Development benchmarking information and communication technologies (ICT) study between November 2013 and January 2014, in Korea. Survey respondents were hospital chief information officers and medical practitioners in primary care clinics. From the national health insurance administrative dataset, two outcomes, the rate of antibiotic prescription and polypharmacy with ≥6 drugs, were extracted. RESULTS: We found that full EMR adoption showed a 16.1% lower antibiotic drug prescription than partial adoption including paper-based medical charts in the hospital only (p = 0.041). Between EMR adoption status and polypharmacy prescription, only those clinics which fully adopted EMR showed significant associations with higher polypharmacy prescriptions (36.9%, p = 0.001). CONCLUSIONS: The findings suggested that there might be some confounding effects present and sophisticated ICT may provide some benefits to the quality of care even with some mixed results. Although a negative relationship between full EMR system adoption and antibiotic drug use was only significant in hospitals, EMR system functions searching drugs or listing specific patients might facilitate antibiotic drug use reduction. Positive relationships between full EMR system adoption and polypharmacy rate in general hospitals and clinics, but not hospitals, require further research.

An openEHR Approach to Detailed Clinical Model Development: Tobacco Smoking Summary Archetype as a Case Study.

BACKGROUND: Data modeling for electronic health records (EHRs) is complex, requiring technological and cognitive sophistication. The openEHR approach leverages the tacit knowledge of domain experts made explicit in a model development process aiming at interoperability and data reuse. OBJECTIVE: The purpose of our research was to explore the process that enabled the aggregation of the tacit knowledge of domain experts in an explicit form using the Clinical Knowledge Manager (CKM) platform and associated assets. The Tobacco Smoking Summary archetype is used to illustrate this. METHODS: Three methods were used to triangulate findings: (1) observation of CKM discussions by crowdsourced domain experts in two reviews, (2) observation of editor discussions and decision-making, and (3) interviews with eight domain experts. CKM discussions were analyzed for content and editor discussions for decision-making, and interviews were thematically analyzed to explore in depth the explication of tacit knowledge. RESULTS: The Detailed Clinical Model (DCM) process consists of a set of reviews by domain experts, with each review followed by editorial discussions and decision-making until an agreement is reached among reviewers and editors that the DCM is publishable. Interviews revealed three themes: (1) data interoperability and reusability, (2) accurate capture of patient data, and (3) challenges of sharing tacit knowledge. DISCUSSION: The openEHR approach to developing an open standard revealed a complex set of conditions for a successful interoperable archetype, such as leadership, maximal dataset, crowdsourced domain expertise and tacit knowledge made explicit, editorial vision, and model-driven software. Aggregated tacit knowledge that is explicated into a DCM enables the ability to collect accurate data and plan for the future. CONCLUSION: The process based on the CKM platform enables domain experts and stakeholders to be heard and to contribute to mutually designed standards that align local protocols and agendas to international interoperability requirements.

The Human Physiome: how standards, software and innovative service infrastructures are providing the building blocks to make it achievable.

Reconstructing and understanding the Human Physiome virtually is a complex mathematical problem, and a highly demanding computational challenge. Mathematical models spanning from the molecular level through to whole populations of individuals must be integrated, then personalized. This requires interoperability with multiple disparate and geographically separated data sources, and myriad computational software tools. Extracting and producing knowledge from such sources, even when the databases and software are readily available, is a challenging task. Despite the difficulties, researchers must frequently perform these tasks so that available knowledge can be continually integrated into the common framework required to realize the Human Physiome. Software and infrastructures that support the communities that generate these, together with their underlying standards to format, describe and interlink the corresponding data and computer models, are pivotal to the Human Physiome being realized. They provide the foundations for integrating, exchanging and re-using data and models efficiently, and correctly, while also supporting the dissemination of growing knowledge in these forms. In this paper, we explore the standards, software tooling, repositories and infrastructures that support this work, and detail what makes them vital to realizing the Human Physiome.

Current National Approach to Healthcare ICT Standardization: Focus on Progress in New Zealand.

OBJECTIVES: Many countries try to efficiently deliver high quality healthcare services at lower and manageable costs where healthcare information and communication technologies (ICT) standardisation may play an important role. New Zealand provides a good model of healthcare ICT standardisation. The purpose of this study was to review the current healthcare ICT standardisation and progress in New Zealand. METHODS: This study reviewed the reports regarding the healthcare ICT standardisation in New Zealand. We also investigated relevant websites related with the healthcare ICT standards, most of which were run by the government. Then, we summarised the governance structure, standardisation processes, and their output regarding the current healthcare ICT standards status of New Zealand. RESULTS: New Zealand government bodies have established a set of healthcare ICT standards and clear guidelines and procedures for healthcare ICT standardisation. Government has actively participated in various enactments of healthcare ICT standards from the inception of ideas to their eventual retirement. Great achievements in eHealth have already been realized, and various standards are currently utilised at all levels of healthcare regionally and nationally. Standard clinical terminologies, such as International Classification of Diseases (ICD) and Systematized Nomenclature of Medicine - Clinical Terms (SNOMED-CT) have been adopted and Health Level Seven (HL7) standards are actively used in health information exchanges. CONCLUSIONS: The government to New Zealand has well organised ICT institutions, guidelines, and regulations, as well as various programs, such as e-Medications and integrated care services. Local district health boards directly running hospitals have effectively adopted various new ICT standards. They might already be benefiting from improved efficiency resulting from healthcare ICT standardisation.

Evaluation of software maintain ability with open EHR - a comparison of architectures.

PURPOSE: To assess whether it is easier to maintain a clinical information system developed using open EHR model driven development versus mainstream methods. METHODS: A new open source application (GastrOS) has been developed following open EHR's multi-level modelling approach using .Net/C# based on the same requirements of an existing clinically used application developed using Microsoft Visual Basic and Access database. Almost all the domain knowledge was embedded into the software code and data model in the latter. The same domain knowledge has been expressed as a set of open EHR Archetypes in GastrOS. We then introduced eight real-world change requests that had accumulated during live clinical usage, and implemented these in both systems while measuring time for various development tasks and change in software size for each change request. RESULTS: Overall it took half the time to implement changes in GastrOS. However it was the more difficult application to modify for one change request, suggesting the nature of change is also important. It was not possible to implement changes by modelling only. Comparison of relative measures of time and software size change within each application highlights how architectural differences affected maintain ability across change requests. CONCLUSIONS: The use of open EHR model driven development can result in better software maintain ability. The degree to which open EHR affects software maintain ability depends on the extent and nature of domain knowledge involved in changes. Although we used relative measures for time and software size, confounding factors could not be totally excluded as a controlled study design was not feasible.

Using a single content model for eHealth interoperability and secondary use.

This chapter describes a middle-out approach to eHealth interoperability, with strong oversight on public health and health research, enabled by a uniform and shared content model to which all health information exchange conforms. As described in New Zealand's Interoperability Reference Architecture, the content model borrows its top level organization from the Continuity of Care Record (CCR) standard and is underpinned by the openEHR formalism. This provides a canonical model for representing a variety of clinical information, and serves as reference when determining payload in health information exchange. The main premise of this approach is that since all exchanged data conforms to the same model, interoperability of clinical information can readily be achieved. Use of Archetypes ensures preservation of clinical context which is critical for secondary use. The content model is envisaged to grow incrementally by adding new or specialised archetypes as finer details are needed in real projects. The consistency and long term viability of this approach critically depends on effective governance which requires new models of collaboration, decision making and appropriate tooling to support the process.

Evaluation of the Turkish translation of the Minimal Standard Terminology for Digestive Endoscopy by development of an endoscopic information system.

BACKGROUND/AIMS: There are very few evaluation studies for the Minimal Standard Terminology for Digestive Endoscopy. This study aims to evaluate the usage of the Turkish translation of Minimal Standard Terminology by developing an endoscopic information system. METHODS: After elicitation of requirements, database modeling and software development were performed. Minimal Standard Terminology driven forms were designed for rapid data entry. The endoscopic report was rapidly created by applying basic Turkish syntax and grammar rules. Entering free text and also editing of final report were possible. After three years of live usage, data analysis was performed and results were evaluated. RESULTS: The system has been used for reporting of all endoscopic examinations. 15,638 valid records were analyzed, including 11,381 esophagogastroduodenoscopies, 2,616 colonoscopies, 1,079 rectoscopies and 562 endoscopic retrograde cholangiopancreatographies. In accordance with other previous validation studies, the overall usage of Minimal Standard Terminology terms was very high: 85% for examination characteristics, 94% for endoscopic findings and 94% for endoscopic diagnoses. Some new terms, attributes and allowed values were also added for better clinical coverage. CONCLUSIONS: Minimal Standard Terminology has been shown to cover a high proportion of routine endoscopy reports. Good user acceptance proves that both the terms and structure of Minimal Standard Terminology were consistent with usual clinical thinking. However, future work on Minimal Standard Terminology is mandatory for better coverage of endoscopic retrograde cholangiopancreatographies examinations. Technically new software development methodologies have to be sought for lowering cost of development and the maintenance phase. They should also address integration and interoperability of disparate information systems.

Effects of lossy image compression on quantitative image analysis of cell nuclei.

OBJECTIVE: To investigate whether statistically significant changes occur in quantitative image analysis of cell nuclei when lossy image compression techniques are used. STUDY DESIGN: Thirty-five stoichiometric, Feulgen-stained samples of rat hepatocytes, human thyroid and ovarian cancer cell nuclei were used. Image analysis was performed by a computerized system that used AutoCyte LINK V1.1.1.56 software (Burlington, North Carolina, U.S.A.) for image acquisition and Zeiss Vision KS 400 V3.0 software (Oberkochen, Germany) for quantitative image analysis. After lossy JPEG compression of acquired images at different quality levels, some densitometric features were selected and measurements performed. RESULTS: We observed that nearly all the standard densitometric features showed statistically significant changes when images were compressed with the lossy JPEG algorithm. However, most invariant densitometric moment features remained free of statistically significant changes. CONCLUSION: The standard densitometric measurements that we used do not tolerate lossy compression. However, analyses using invariant densitometric features may be performed on images compressed with lossy JPEG, resulting in simpler, less expensive systems demanding less network bandwidth.