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Consumer-Generated Whole-Person Health Data: A Structured Approach
Published in Connie White Delaney, Charlotte A. Weaver, Joyce Sensmeier, Lisiane Pruinelli, Patrick Weber, Nursing and Informatics for the 21st Century – Embracing a Digital World, 3rd Edition, Book 3, 2022
Robin Austin, Sripriya Rajamani, Karen A. Monsen
Using a simple ontology to characterize whole-person health and healthcare has several benefits. First, characterizing all of health and healthcare in a simple ontology creates a knowable information model for information management for clinicians and researchers alike. Second, comprehending the whole leads to the identification of gaps in existing data that may be critical for understanding the whole. The siloes in healthcare research and practice can be diminished by providing shared language to bring clarity regarding the meanings of concepts and relationships as operationalized in clinical decision support, documentation and research (Reed, 1997; Martin, 2005; Matney et al., 2011). One such ontology is a multidisciplinary SNT recognized by the American Nurses Association (2012): the Omaha System (Figure 3.1).
Intelligent Data Analysis Techniques
Published in Arvind Kumar Bansal, Javed Iqbal Khan, S. Kaisar Alam, Introduction to Computational Health Informatics, 2019
Arvind Kumar Bansal, Javed Iqbal Khan, S. Kaisar Alam
Ontology is defined as the study of beings that means a study of entities, their relationships, and equivalence of the groups of entities and their relationships to other groups of entities. This involves understanding the meaning of the words, discovering relationships between the words and phrases using synonyms, antonyms and class-hierarchies. The equivalences of words and phrases are expressed as a knowledge-base that uses rules and algorithms to traverse up and down the class-hierarchy relationships.
Theoretical and methodological challenges
Published in Kirsti Malterud, Qualitative Metasynthesis, 2019
Ontology is a concept from the philosophy of science that refers to how we understand the world and reality. Epistemology is the analogous concept for advancing knowledge about the world and reality. Consistency between ontology and epistemology is hence a logical prerequisite for scientific knowledge (Malterud, 2019). How we recognize the world will determine relevant and valid approaches for studying the world and understanding reality, and the other way round: the perspectives embedded in our knowledge about the world will have an impact on how we understand and perceive it.
Machine intelligence for radiation science: summary of the Radiation Research Society 67th annual meeting symposium
Published in International Journal of Radiation Biology, 2023
Lydia J. Wilson, Frederico C. Kiffer, Daniel C. Berrios, Abigail Bryce-Atkinson, Sylvain V. Costes, Olivier Gevaert, Bruno F. E. Matarèse, Jack Miller, Pritam Mukherjee, Kristen Peach, Paul N. Schofield, Luke T. Slater, Britta Langen
The application of ML and MI to problems in radiobiology requires the development and mobilization of extremely large, coherent, and interoperable datasets for training and testing. Some such datasets are already available, and new ones are being produced at pace (Schofield et al. 2019). We face two challenges: identifying and integrating these very large datasets, and using them to support analysis using MI. Formal ontologies (such as the major bioinformatics initiative Gene Ontology, https://geneontology.org), provide powerful metadata models, and can be used to annotate data subjects with categorical descriptors (e.g. pathology or anatomy terms), or to qualify numeric data as to data type, units etc. This semantic annotation supports the creation of large knowledge graphs and integration of background knowledge, facilitating data discovery and knowledge synthesis across distributed databases.
A Systematic Review of Black American Same-Sex Couples Research: Laying the Groundwork for Culturally-Specific Research and Interventions
Published in The Journal of Sex Research, 2022
Jonathan Mathias Lassiter, Jagadīśa-devaśrī Dacus, Mallory O. Johnson
The philosophical assumptions (i.e., ontological, epistemological, axiological, and methodological) that underpin a scientific knowledgebase are particularly important as they are directly linked to the cultural frameworks and contexts researchers use to understand themselves and the subjects of their investigations. Briefly, ontology concerns the nature of reality, epistemology queries how we know what we know, axiology highlights our values in research, and methodology focuses on the processes we use in research (Creswell & Poth, 2017). These assumptions guide the ways in which policymakers and scientists prioritize health topics, methodologies, and dissemination outlets. For example, a deficit approach to public health research has been highlighted as potentially leading to a negative view of marginalized people, such as BASCs, by over-focusing on what is going wrong instead of acknowledging and leveraging strengths (Herrick et al., 2014). A research landscape that prioritizes deficit-based understandings of health possibly reveals a view of reality as adversity-laden and harmful with a value on individual over context, and avoidance of negative consequences (Herrick et al., 2014). Thus, strengths-based approaches and other research paradigms informed by philosophical assumptions that prioritize positive interactions and outcomes may be systematically overlooked in such a deficit-based research landscape.
Ontology: A Bridge between Bioethics and Data-Driven Inquiry
Published in The American Journal of Bioethics, 2021
Eric C. Merrell, Peter Maloy Koch, David Gordon Limbaugh
The second, and perhaps most important, benefit that ontology brings to design bioethics is that they enable data sharing and use among parties that utilize the same ontologies. Realist ontologies are particularly well-suited to promote interoperability because they are grounded in general categories that are found across disciplines and domains, rather than in specific and often esoteric field-dependent categories. For example, many OBO Foundry ontologies are sub-ontologies of the Ontology of General Medical Sciences (OGMS), which defines high level terms in medicine, like “disorder,” “disease,” and “disease progression” (Scheuermann, Ceusters, and Smith 2009). OGMS is in turn a sub-ontology of Basic Formal Ontology, which defines terms like “function,” “material entity,” and “process” (Arp, Smith, and Spear 2015). Exploiting this nested structure allows for the introduction of specific ontologies and terms for specific projects with a promise that those terms will be interoperable with other diverse data.