ACLRO: An Ontology for the Best Practice in ACLR Rehabilitation

dc.contributor.advisorJones, Josette F.
dc.contributor.authorPhalakornkule, Kanitha
dc.contributor.otherBoukai, Ben
dc.contributor.otherLiu, Xiaowen
dc.contributor.otherPurkayatha, Saptarshi
dc.contributor.otherDuncan, William D.
dc.date.accessioned2020-12-14T16:20:58Z
dc.date.available2020-12-14T16:20:58Z
dc.date.issued2020-10
dc.degree.date2020en_US
dc.degree.discipline
dc.degree.grantorIndiana Universityen_US
dc.degree.levelPh.D.en_US
dc.descriptionIndiana University-Purdue University Indianapolis (IUPUI)en_US
dc.description.abstractWith 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.en_US
dc.identifier.urihttps://hdl.handle.net/1805/24614
dc.identifier.urihttp://dx.doi.org/10.7912/C2/964
dc.language.isoen_USen_US
dc.subjectBFOen_US
dc.subjectInformation Integrationen_US
dc.subjectKnowledge Representationen_US
dc.subjectOntologyen_US
dc.subjectSemantic Weben_US
dc.subjectTaxonomyen_US
dc.titleACLRO: An Ontology for the Best Practice in ACLR Rehabilitationen_US
dc.typeDissertation
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