상세정보
학술대회
Automatic Stroke Medical Ontology Augmentation with Standard Medical Terminology and Unstructured Textual Medical Knowledge
- 발행일
- 202108
- 출처
- International Conference on Platform Technology and Service (PlatCon) 2021, pp.19-23
- 초록
- The need for medical ontology to provide stroke medical knowledge is increasing as much research has recently been conducted to predict stroke diseases using AI technology quickly. Medical ontology serves as a medical explanation of predictions in conjunction with methods of analysis using machine learning and deep learning to analyze clinical data obtained from the medical field, medical imaging devices (MRI, CT, ultrasound, etc.). However, the existing medical ontology focused on is-A relationships in taxonomy to define the classification system for diseases, symptoms, and anatomical structures. This medical ontology is insufficient to explain complex organic relationships to disease-symptom-body-patients, a knowledge structure for predicting disease. Furthermore, although professional standard terms exist in medicine, electronic medical records (EMR), electronic health records (EHR) medical professional books, and medical papers that use common terms to express professional are mostly unstructured forms. To overcome this limitation, in this paper, we propose a stroke medical ontology automatic augmentation method via unstructured text medical knowledge using the lowest instance-level medical term ontology and top-level schema-level medical ontology for stroke disease prediction through standard medical terms. The proposed method extracts and stores data in resource description framework (RDF) form with unstructured textual medical knowledge (medical papers, medical professional books), health data, and syntactic morphology analysis of clinical data, with instance-level ontologies capable of linking top-level schema to standard medical terminology ontologies such as the international classification diseases (ICD), systematized nomenclature of medicine-clinical terms (SNOMED-CT), and foundational model of anatomy (FMA). We also use a medical data-knowledge mapping DB that stores the frequency of extracted data torches for the abstraction of extracted RDF data.