SNOMED CT standard ontology based on the ontology for general medical science

Background: Systematized Nomenclature of Medicine—Clinical Terms (SNOMED CT, hereafter abbreviated SCT) is acomprehensive medical terminology used for standardizing the storage, retrieval, and exchange of electronic healthdata. Some efforts have been made to capture the contents of SCT as Web Ontology Language (OWL), but theseefforts have been hampered by the size and complexity of SCT. Method: Our proposal here is to develop an upper-level ontology and to use it as the basis for defining the termsin SCT in a way that will support quality assurance of SCT, for example, by allowing consistency checks ofdefinitions and the identification and elimination of redundancies in the SCT vocabulary. Our proposed upper-levelSCT ontology (SCTO) is based on the Ontology for General Medical Science (OGMS). Results: The SCTO is implemented in OWL 2, to support automatic inference and consistency checking. Theapproach will allow integration of SCT data with data annotated using Open Biomedical Ontologies (OBO) Foundryontologies, since the use of OGMS will ensure consistency with the Basic Formal Ontology, which is the top-levelontology of the OBO Foundry. Currently, the SCTO contains 304 classes, 28 properties, 2400 axioms, and 1555annotations. It is publicly available through the bioportal athttp://bioportal.bioontology.org/ontologies/SCTO/. Conclusion: The resulting ontology can enhance the semantics of clinical decision support systems and semanticinteroperability among distributed electronic health records. In addition, the populated ontology can be used forthe automation of mobile health applications

Conceptual graph-based knowledge representation for supporting reasoning in African traditional medicine

Although African patients use both conventional or modern and traditional healthcare simultaneously, it has been proven that 80% of people rely on African traditional medicine (ATM). ATM includes medical activities stemming from practices, customs and traditions which were integral to the distinctive African cultures. It is based mainly on the oral transfer of knowledge, with the risk of losing critical knowledge. Moreover, practices differ according to the regions and the availability of medicinal plants. Therefore, it is necessary to compile tacit, disseminated and complex knowledge from various Tradi-Practitioners (TP) in order to determine interesting patterns for treating a given disease. Knowledge engineering methods for traditional medicine are useful to model suitably complex information needs, formalize knowledge of domain experts and highlight the effective practices for their integration to conventional medicine. The work described in this paper presents an approach which addresses two issues. First it aims at proposing a formal representation model of ATM knowledge and practices to facilitate their sharing and reusing. Then, it aims at providing a visual reasoning mechanism for selecting best available procedures and medicinal plants to treat diseases. The approach is based on the use of the Delphi method for capturing knowledge from various experts which necessitate reaching a consensus. Conceptual graph formalism is used to model ATM knowledge with visual reasoning capabilities and processes. The nested conceptual graphs are used to visually express the semantic meaning of Computational Tree Logic (CTL) constructs that are useful for formal specification of temporal properties of ATM domain knowledge. Our approach presents the advantage of mitigating knowledge loss with conceptual development assistance to improve the quality of ATM care (medical diagnosis and therapeutics), but also patient safety (drug monitoring)

The experience and impact of chronic disease peer support interventions: A qualitative synthesis

OBJECTIVE: Our aim was to synthesise qualitative literature about the perceived impact and experience of participating in peer support interventions for individuals with chronic disease. METHODS: We carried out a meta-ethnography to synthesize 25 papers meeting specific inclusion criteria. RESULTS: Thirteen concepts were identified that reflected participants' perceptions of the experience and impact of intervention participation. These were brought together in a conceptual model that highlighted both positive and negative perceptions, while also indicating if specific experiences and impacts had greater pertinence for mentors, mentees, or were mutually experienced. CONCLUSION: Although peer support interventions may establish uneven power relationships between mentors and mentees, there is also potential for initially asymmetrical relationships to become more symmetrical over time. Our synthesis suggests that emotional support is particularly valued when delivered under conditions that do not merely reproduce biomedical hierarchies of power. PRACTICE IMPLICATIONS: This synthesis suggests that those developing and implementing peer support interventions need to be sensitive to their potential negative effects. They will need to manage the tension between the hierarchical and egalitarian aspects of peer support interventions, and consider the impact on both mentors and mentees

Ontology-based knowledge representation of experiment metadata in biological data mining

According to the PubMed resource from the U.S. National Library of Medicine, over 750,000 scientific articles have been published in the ~5000 biomedical journals worldwide in the year 2007 alone. The vast majority of these publications include results from hypothesis-driven experimentation in overlapping biomedical research domains. Unfortunately, the sheer volume of information being generated by the biomedical research enterprise has made it virtually impossible for investigators to stay aware of the latest findings in their domain of interest, let alone to be able to assimilate and mine data from related investigations for purposes of meta-analysis. While computers have the potential for assisting investigators in the extraction, management and analysis of these data, information contained in the traditional journal publication is still largely unstructured, free-text descriptions of study design, experimental application and results interpretation, making it difficult for computers to gain access to the content of what is being conveyed without significant manual intervention. In order to circumvent these roadblocks and make the most of the output from the biomedical research enterprise, a variety of related standards in knowledge representation are being developed, proposed and adopted in the biomedical community. In this chapter, we will explore the current status of efforts to develop minimum information standards for the representation of a biomedical experiment, ontologies composed of shared vocabularies assembled into subsumption hierarchical structures, and extensible relational data models that link the information components together in a machine-readable and human-useable framework for data mining purposes

A HIERACHICAL MODEL FOR MEDICAL REGISTRATIONS

The aim of this paper is to improve solutions for developing and improving medical and pharmaceutical services. We made a SWOT analyze of SIUI in order to build a document management system and create medical registration papers, based on collaborative editing and international medical standard. This model was focused on hierarchical decomposition of PHR and EHR records, using modular solution, which stores all data in XML files. It requires a system that is simple to use and allows users to focus their efforts on the content rather that on the technology used to create it. This approach allows a great flexibility in handling document and user interaction.medical registrations, HME standards, PHR, SIUI, hierarchical decomposition

Modeling Faceted Browsing with Category Theory for Reuse and Interoperability

Faceted browsing (also called faceted search or faceted navigation) is an exploratory search model where facets assist in the interactive navigation of search results. Facets are attributes that have been assigned to describe resources being explored; a faceted taxonomy is a collection of facets provided by the interface and is often organized as sets, hierarchies, or graphs. Faceted browsing has become ubiquitous with modern digital libraries and online search engines, yet the process is still difficult to abstractly model in a manner that supports the development of interoperable and reusable interfaces. We propose category theory as a theoretical foundation for faceted browsing and demonstrate how the interactive process can be mathematically abstracted in order to support the development of reusable and interoperable faceted systems. Existing efforts in facet modeling are based upon set theory, formal concept analysis, and light-weight ontologies, but in many regards they are implementations of faceted browsing rather than a specification of the basic, underlying structures and interactions. We will demonstrate that category theory allows us to specify faceted objects and study the relationships and interactions within a faceted browsing system. Resulting implementations can then be constructed through a category-theoretic lens using these models, allowing abstract comparison and communication that naturally support interoperability and reuse. In this context, reuse and interoperability are at two levels: between discrete systems and within a single system. Our model works at both levels by leveraging category theory as a common language for representation and computation. We will establish facets and faceted taxonomies as categories and will demonstrate how the computational elements of category theory, including products, merges, pushouts, and pullbacks, extend the usefulness of our model. More specifically, we demonstrate that categorical constructions such as the pullback and pushout operations can help organize and reorganize facets; these operations in particular can produce faceted views containing relationships not found in the original source taxonomy. We show how our category-theoretic model of facets relates to database schemas and discuss how this relationship assists in implementing the abstractions presented. We give examples of interactive interfaces from the biomedical domain to help illustrate how our abstractions relate to real-world requirements while enabling systematic reuse and interoperability. We introduce DELVE (Document ExpLoration and Visualization Engine), our framework for developing interactive visualizations as modular Web-applications in order to assist researchers with exploratory literature search. We show how facets relate to and control visualizations; we give three examples of text visualizations that either contain or interact with facets. We show how each of these visualizations can be represented with our model and demonstrate how our model directly informs implementation. With our general framework for communicating consistently about facets at a high level of abstraction, we enable the construction of interoperable interfaces and enable the intelligent reuse of both existing and future efforts

Decolonizing Stigma and Diagnosis as Healing Work

In order to disrupt dominant understandings of health and well-being, and to confront systemic injustices that result in ongoing health inequities, stigma must be addressed from both within and beyond the realm of medical diagnosis. The individualistic nature of diagnosis, that is characteristic of Western medical approaches, often perpetuates stigma. The role of diagnosis in biomedicine, as well as the historicity of professions and disciplines in Westernized health-care, intersect with different hierarchies of power, identities, and knowledges through mechanisms that operate across local and global contexts. This paper argues that a decolonial approach to health research, practice and education offers an important lens through which to critically analyse these intersections of power, identities, and knowledges. Such an approach can help disrupt dominant understandings of health and well-being. To advance the argument, examples of decolonial thinking approaches and pedagogical methods from South Africa are provided

Developing The Language And Tools To Address Food Insecurity

There is vast literature indicating associations of food insecurity with concerning health outcomes, and clinic and community settings now regularly partner to address food insecurity as it is assessed. However, there is scarce health terminology to use in the care of patients experiencing food insecurity. This presents challenges as clinicians seek to define food insecurity as a risk for their patients, order interventions to address it, and study the effect of interventions in individual and population settings. Furthermore, there is no published food insecurity diagnostic criteria to employ as clinicians listen to the histories of their patients and try to support them in being well. This project endeavored to complete three aims: apply for ICD-10-CM terminology for food insecurity and related health concerns, apply for SNOMED CT terminology for key interventions, and forge initial considerations for a food insecurity diagnostic criteria. The author initially proceeded independently, but their efforts soon became embedded in the national consensus social determinant of health (SDOH) data initiative, the Gravity Project. The author served as one of two food insecurity subject matter experts. The Gravity Project worked collaboratively with data standard organizations to identify a comprehensive data set of 24 screening tools, six goals of care, eight diagnoses, and 109 interventions. In tandem, the author worked with key national content experts to develop diagnostic criteria considerations and a pathway for criteria development

A conceptual treadmill: the need for ‘middle ground’ in clinical decision making theory in nursing

This paper explores the two predominant theoretical approaches to the process of nurse decision making prevalent within the nursing research literature: systematic-positivistic approaches as exemplifed by information processing theory, and the intuitive-humanistic approach of Patricia Benner. The two approaches' strengths and weaknesses are explored and as a result a third theoretical stance is proffered: the idea of a cognitive continuum. According to this approach the systematic and intuitive theoretical camps occupy polar positions at either end of a continuum as opposed to separate theoretical planes. The methodological and professional benefits of adopting such a stance are also briefly outlined

‘Good’ evidence for improved policy making: from hierarchies to appropriateness

Within the field of public health, and increasingly across other areas of social policy, there are widespread calls to increase or improve the use of evidence for policy making. Often these calls rest on an assumption that improved evidence utilisation will be a more efficient or effective means of achieving social goals. Yet, a clear elucidation of what can be considered ‘good evidence’ for policy use is rarely articulated. Many of the current discussions of best practice in the health policy sector derive from the evidence-based medicine (EBM) movement, embracing the ‘hierarchy of evidence’ in framing the selection of evidence – a hierarchy that places experimental trials as preeminent in terms of methodological quality. However, there are a number of difficulties associated with applying EBM methods of grading evidence onto policy making. Numerous public health authors have noted that the hierarchy of evidence is a judgement of quality specifically developed for measuring intervention effectiveness, and as such it cannot address other important health policy considerations such as affordability, salience, or public acceptability (Petticrew and Roberts, 2003). Social scientists and philosophers of knowledge have illustrated other problems in the direct application of the hierarchy of evidence to guide policy. Complex or structural interventions are often not conducive to experimental methods, and as such, a focus on evidence derived from randomised trials may shift policy attention away from broader structural issues (such as addressing the social determinants of health (Solar and Irwin, 2007)), to disease treatment or single element interventions. Social and behavioural interventions also present external validity problems to experimental methods and meta-analyses, as the mechanisms by which an intervention works in one social context may be very different or produce different results elsewhere (Cartwright, 2011). In these cases, policy makers may be better advised to look for evidence about the mechanism of effect, and evidence of local contextual features (Pawson et al., 2005). We argue that rather than adhering to a single hierarchy of evidence to judge what constitutes ‘good’ evidence for policy, it is more useful to examine evidence through the lens of appropriateness. It is important to utilise evidence to improve policy outcomes, yet the form of that evidence should vary depending on the multiple decision criteria at stake. Policy makers must therefore start by articulating their decision criteria in relation to a given problem or policy, so that the appropriate forms of evidence can be drawn on – from both epidemiological and clinical experiments (e.g. for questions of treatment effect), as well as from social scientific, social epidemiological, and multidisciplinary sources (e.g. for questions of complex causality, acceptability, human rights, etc.). Following this selection of types of evidence on the basis of appropriateness, the rigour and quality of the research can be assessed according to the evidentiary best practice standards of the discipline within which the evidence was produced. This approach speaks to calls to improve the use of evidence through ensuring rigour and methodological quality, yet recognises that good evidence is dictated by specific public health or social policy goals