Browsing by Subject "Ontology"
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Item ACLRO: An Ontology for the Best Practice in ACLR Rehabilitation(2020-10) Phalakornkule, Kanitha; Jones, Josette F.; Boukai, Ben; Liu, Xiaowen; Purkayatha, Saptarshi; Duncan, William D.With the rise of big data and the demands for leveraging artificial intelligence (AI), healthcare requires more knowledge sharing that offers machine-readable semantic formalization. Even though some applications allow shared data interoperability, they still lack formal machine-readable semantics in ICD9/10 and LOINC. With ontology, the further ability to represent the shared conceptualizations is possible, similar to SNOMED-CT. Nevertheless, SNOMED-CT mainly focuses on electronic health record (EHR) documenting and evidence-based practice. Moreover, due to its independence on data quality, the ontology enhances advanced AI technologies, such as machine learning (ML), by providing a reusable knowledge framework. Developing a machine-readable and sharable semantic knowledge model incorporating external evidence and individual practice’s values will create a new revolution for best practice medicine. The purpose of this research is to implement a sharable ontology for the best practice in healthcare, with anterior cruciate ligament reconstruction (ACLR) as a case study. The ontology represents knowledge derived from both evidence-based practice (EBP) and practice-based evidence (PBE). First, the study presents how the domain-specific knowledge model is built using a combination of Toronto Virtual Enterprise (TOVE) and a bottom-up approach. Then, I propose a top-down approach using Open Biological and Biomedical Ontology (OBO) Foundry ontologies that adheres to the Basic Formal Ontology (BFO)’s framework. In this step, the EBP, PBE, and statistic ontologies are developed independently. Next, the study integrates these individual ontologies into the final ACLR Ontology (ACLRO) as a more meaningful model that endorses the reusability and the ease of the model-expansion process since the classes can grow independently from one another. Finally, the study employs a use case and DL queries for model validation. The study's innovation is to present the ontology implementation for best-practice medicine and demonstrate how it can be applied to a real-world setup with semantic information. The ACLRO simultaneously emphasizes knowledge representation in health-intervention, statistics, research design, and external research evidence, while constructing the classes of data-driven and patient-focus processes that allow knowledge sharing explicit of technology. Additionally, the model synthesizes multiple related ontologies, which leads to the successful application of best-practice medicine.Item Alzheimer's Disease Narratives and the Myth of Human Being(2012-12-11) Rieske, Tegan Echo; Schultz, Jane E.; Johnson, Karen Ramsay; Tilley, John J.The ‘loss of self’ trope is a pervasive shorthand for the prototypical process of Alzheimer's disease (AD) in the popular imagination. Turned into an effect of disease, the disappearance of the self accommodates a biomedical story of progressive deterioration and the further medicalization of AD, a process which has been storied as an organic pathology affecting the brain or, more recently, a matter of genetic calamity. This biomedical discourse of AD provides a generic framework for the disease and is reproduced in its illness narratives. The disappearance of self is a mythic element in AD narratives; it necessarily assumes the existence of a singular and coherent entity which, from the outside, can be counted as both belonging to and representing an individual person. The loss of self, as the rhetorical locus of AD narrative, limits the privatization of the experience and reinscribes cultural storylines---storylines about what it means to be a human person. The loss of self as it occurs in AD narratives functions most effectively in reasserting the presence of the human self, in contrast to an anonymous, inhuman nonself; as AD discourse details a loss of self, it necessarily follows that the thing which is lost (the self) always already existed. The private, narrative self of individual experience thus functions as proxy to a collective human identity predicated upon exceptionalism: an escape from nature and the conditions of the corporeal environment.Item A computable pathology report for precision medicine: extending an observables ontology unifying SNOMED CT and LOINC(Oxford, 2017-09-13) Campbell, Walter S; Karlsson, Daniel; Vreeman, Daniel J; Lazenby, Audrey J; Talmon, Geoffrey A; Campbell, James R; Medicine, School of MedicineBackground The College of American Pathologists (CAP) introduced the first cancer synoptic reporting protocols in 1998. However, the objective of a fully computable and machine-readable cancer synoptic report remains elusive due to insufficient definitional content in Systematized Nomenclature of Medicine – Clinical Terms (SNOMED CT) and Logical Observation Identifiers Names and Codes (LOINC). To address this terminology gap, investigators at the University of Nebraska Medical Center (UNMC) are developing, authoring, and testing a SNOMED CT observable ontology to represent the data elements identified by the synoptic worksheets of CAP. Methods Investigators along with collaborators from the US National Library of Medicine, CAP, the International Health Terminology Standards Development Organization, and the UK Health and Social Care Information Centre analyzed and assessed required data elements for colorectal cancer and invasive breast cancer synoptic reporting. SNOMED CT concept expressions were developed at UNMC in the Nebraska Lexicon© SNOMED CT namespace. LOINC codes for each SNOMED CT expression were issued by the Regenstrief Institute. SNOMED CT concepts represented observation answer value sets. Results UNMC investigators created a total of 194 SNOMED CT observable entity concept definitions to represent required data elements for CAP colorectal and breast cancer synoptic worksheets, including biomarkers. Concepts were bound to colorectal and invasive breast cancer reports in the UNMC pathology system and successfully used to populate a UNMC biobank. Discussion The absence of a robust observables ontology represents a barrier to data capture and reuse in clinical areas founded upon observational information. Terminology developed in this project establishes the model to characterize pathology data for information exchange, public health, and research analytics.Item The Cultural Influences that Provide the Impetus to Create Self-Identity Through Inscribing the Body(2010-07-19T15:34:22Z) Doran, Teri Lynn; Dobris, Catherine A.; White-Mills, Kim D.; Parrish-Sprowl, JohnTattoos, a permanent body modification that has frequently been associated with deviance and lower class sub-cultures, have become increasingly popular in the United States since the early 1990’s. In my thesis I examine the shared worldviews of individuals who obtain tattoos by conducting an analysis of six internet communities that promote this sub-culture in order to understand how cultural influences provide the impetus to create self-identity through inscribing the body. I will argue that individuals who commit to a permanent tattoo may be motivated by the need to create self identity.Item Lantern: an integrative repository of functional annotations for lncRNAs in the human genome(BMC, 2021-05-26) Daulatabad, Swapna Vidhur; Srivastava, Rajneesh; Janga, Sarath Chandra; BioHealth Informatics, School of Informatics and ComputingBackground: With advancements in omics technologies, the range of biological processes where long non-coding RNAs (lncRNAs) are involved, is expanding extensively, thereby generating the need to develop lncRNA annotation resources. Although, there are a plethora of resources for annotating genes, despite the extensive corpus of lncRNA literature, the available resources with lncRNA ontology annotations are rare. Results: We present a lncRNA annotation extractor and repository (Lantern), developed using PubMed's abstract retrieval engine and NCBO's recommender annotation system. Lantern's annotations were benchmarked against lncRNAdb's manually curated free text. Benchmarking analysis suggested that Lantern has a recall of 0.62 against lncRNAdb for 182 lncRNAs and precision of 0.8. Additionally, we also annotated lncRNAs with multiple omics annotations, including predicted cis-regulatory TFs, interactions with RBPs, tissue-specific expression profiles, protein co-expression networks, coding potential, sub-cellular localization, and SNPs for ~ 11,000 lncRNAs in the human genome, providing a one-stop dynamic visualization platform. Conclusions: Lantern integrates a novel, accurate semi-automatic ontology annotation engine derived annotations combined with a variety of multi-omics annotations for lncRNAs, to provide a central web resource for dissecting the functional dynamics of long non-coding RNAs and to facilitate future hypothesis-driven experiments. The annotation pipeline and a web resource with current annotations for human lncRNAs are freely available on sysbio.lab.iupui.edu/lantern.Item Mining Biomedical Literature to Extract Pharmacokinetic Drug-Drug Interactions(2014-02-03) Karnik, Shreyas; Li, Lang; Liu, Yunlong; Liu, XiaowenPolypharmacy is a general clinical practice, there is a high chance that multiple administered drugs will interfere with each other, such phenomenon is called drug-drug interaction (DDI). DDI occurs when drugs administered change each other's pharmacokinetic (PK) or pharmacodynamic (PD) response. DDIs in many ways affect the overall effectiveness of the drug or at some times pose a risk of serious side effects to the patients thus, it becomes very challenging to for the successful drug development and clinical patient care. Biomedical literature is rich source for in-vitro and in-vivo DDI reports and there is growing need to automated methods to extract the DDI related information from unstructured text. In this work we present an ontology (PK ontology), which defines annotation guidelines for annotation of PK DDI studies. Using the ontology we have put together a corpora of PK DDI studies, which serves as excellent resource for training machine learning, based DDI extraction algorithms. Finally we demonstrate the use of PK ontology and corpora for extracting PK DDIs from biomedical literature using machine learning algorithms.Item An ontology for formal representation of medication adherence-related knowledge : case study in breast cancer(2018-08-02) Sawesi, Suhila; MacDorman, Karl F.; Jones, Josette F.; Carpenter, Janet S.; Kharrazi, Hadi; Duncan, William D.Medication non-adherence is a major healthcare problem that negatively impacts the health and productivity of individuals and society as a whole. Reasons for medication non-adherence are multi-faced, with no clear-cut solution. Adherence to medication remains a difficult area to study, due to inconsistencies in representing medicationadherence behavior data that poses a challenge to humans and today’s computer technology related to interpreting and synthesizing such complex information. Developing a consistent conceptual framework to medication adherence is needed to facilitate domain understanding, sharing, and communicating, as well as enabling researchers to formally compare the findings of studies in systematic reviews. The goal of this research is to create a common language that bridges human and computer technology by developing a controlled structured vocabulary of medication adherence behavior—“Medication Adherence Behavior Ontology” (MAB-Ontology) using breast cancer as a case study to inform and evaluate the proposed ontology and demonstrating its application to real-world situation. The intention is for MAB-Ontology to be developed against the background of a philosophical analysis of terms, such as belief, and desire to be human, computer-understandable, and interoperable with other systems that support scientific research. The design process for MAB-Ontology carried out using the METHONTOLOGY method incorporated with the Basic Formal Ontology (BFO) principles of best practice. This approach introduces a novel knowledge acquisition step that guides capturing medication-adherence-related data from different knowledge sources, including adherence assessment, adherence determinants, adherence theories, adherence taxonomies, and tacit knowledge source types. These sources were analyzed using a systematic approach that involved some questions applied to all source types to guide data extraction and inform domain conceptualization. A set of intermediate representations involving tables and graphs was used to allow for domain evaluation before implementation. The resulting ontology included 629 classes, 529 individuals, 51 object property, and 2 data property. The intermediate representation was formalized into OWL using Protégé. The MAB-Ontology was evaluated through competency questions, use-case scenario, face validity and was found to satisfy the requirement specification. This study provides a unified method for developing a computerized-based adherence model that can be applied among various disease groups and different drug categories.Item Strategies and foundations for scientific discovery in longitudinal studies of bipolar disorder(Wiley, 2022) McInnis, Melvin G.; Andreassen, Ole A.; Andreazza, Ana C.; Alon, Uri; Berk, Michael; Brister, Teri; Burdick, Katherine E.; Cui, Donghong; Frye, Mark; Leboyer, Marion; Mitchell, Philip B.; Merikangas, Kathleen; Nierenberg, Andrew A.; Nurnberger, John I.; Pham, Daniel; Vieta, Eduard; Yatham, Lakshmi N.; Young, Allan H.; Psychiatry, School of MedicineBipolar disorder (BD) is a complex and dynamic condition with a typical onset in late adolescence or early adulthood followed by an episodic course with intervening periods of subthreshold symptoms or euthymia. It is complicated by the accumulation of comorbid medical and psychiatric disorders. The etiology of BD remains unknown and no reliable biological markers have yet been identified. This is likely due to lack of comprehensive ontological framework and, most importantly, the fact that most studies have been based on small nonrepresentative clinical samples with cross‐sectional designs. We propose to establish large, global longitudinal cohorts of BD studied consistently in a multidimensional and multidisciplinary manner to determine etiology and help improve treatment. Herein we propose collection of a broad range of data that reflect the heterogenic phenotypic manifestations of BD that include dimensional and categorical measures of mood, neurocognitive, personality, behavior, sleep and circadian, life‐story, and outcomes domains. In combination with genetic and biological information such an approach promotes the integrating and harmonizing of data within and across current ontology systems while supporting a paradigm shift that will facilitate discovery and become the basis for novel hypotheses.