Lazarova, S.; Petrova-Antonova, D.; Kunchev, T. Ontology-Driven Knowledge Sharing in Alzheimer’s Disease Research. Information2023, 14, 188.
Lazarova, S.; Petrova-Antonova, D.; Kunchev, T. Ontology-Driven Knowledge Sharing in Alzheimer’s Disease Research. Information 2023, 14, 188.
Lazarova, S.; Petrova-Antonova, D.; Kunchev, T. Ontology-Driven Knowledge Sharing in Alzheimer’s Disease Research. Information2023, 14, 188.
Lazarova, S.; Petrova-Antonova, D.; Kunchev, T. Ontology-Driven Knowledge Sharing in Alzheimer’s Disease Research. Information 2023, 14, 188.
Abstract
Alzheimer’s disease is a debilitating neurodegenerative condition which is known to be the most common cause of dementia. Despite its rapidly growing prevalence, medicine still lacks a comprehensive definition of the disease. As a result, Alzheimer’s disease remains neither preventable nor curable. In recent years, broad interdisciplinary collaborations in Alzheimer’s disease research are becoming more common. Furthermore, such collaborations have already demonstrated their superiority in addressing the complexity of the disease in innovative ways. However, establishing effective communication and optimal knowledge distribution between researchers and specialists with different expertise and background is not a straightforward task. To address this challenge, we propose the Alzheimer’s disease Ontology for Diagnosis and Preclinical Classification (AD-DPC) as a tool for effective knowledge sharing in interdisciplinary/multidisciplinary teams working on Alzheimer’s disease. It covers six major conceptual groups, namely Alzheimer's disease pathology, Alzheimer's disease spectrum, Diagnostic process, Symptoms, Assessments, and Relevant clinical findings. All concepts were annotated with definitions or elucidations and in some cases enriched with synonyms and additional resources. The potential of AD-DPC to support non-medical experts is demonstrated through an evaluation of its usability, applicability and correctness. The results show that the participants in the evaluation process who lack prior medical knowledge can successfully answer Alzheimer’s disease-related questions by interacting with AD-DPC. Furthermore, their perceived level of knowledge in the field increased leading to effective communication with medical experts.
Computer Science and Mathematics, Information Systems
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