ارسل ملاحظاتك

ارسل ملاحظاتك لنا







الرئيسية > An Encoding Methodology for Me...>الوصف

An Encoding Methodology for Medical Knowledge Using SNOMED CT Ontology

المصدر: مجلة جامعة الملك سعود - علوم الحاسب والمعلومات
الناشر: جامعة الملك سعود
المؤلف الرئيسي: El-Sappagh, Shaker H. Ali (Author)
مؤلفين آخرين: Elmogy, Mohammed (Co-Author)
المجلد/العدد: مج28, ع3
محكمة: نعم
الدولة: السعودية
التاريخ الميلادي: 2016
الصفحات: 311 - 329
DOI: 10.33948/0584-028-003-007
ISSN: 1319-1578
رقم MD: 973952
نوع المحتوى: بحوث ومقالات
اللغة: الإنجليزية
قواعد المعلومات: science
مواضيع:
الأنطولوجيا | الخوارزميات | تشخيص الأمراض | مرض السكري
كلمات المؤلف المفتاحية:
Clinical Decision Support System (CDSS) | SNOMED CT (SCT) Coding | Semantic Data Retrieval | Ontology | Case Based Reasoning (CBR) | Diabetes Diagnosis
رابط المحتوى:
PDF (صورة)     
صورة الغلاف QR قانون
حفظ في:
المستخلص: Knowledge-Intensive Case Based Reasoning (KI-CBR) systems mainly depend on ontology. Using ontology as domain knowledge supports the implementation of semantically intelligent case retrieval algorithms. The case-based knowledge must be encoded with the same concepts of the domain ontology. Standard medical ontologies, such as SNOMED CT (SCT), can play the role of domain ontology to enhance case representation and retrieval. This study has three stages. First, we propose an encoding methodology using SCT. Second, this methodology is used to encode the case-based knowledge. Third, all the used SCT concepts are collected in a reference set, and an O0057L2 ontology of 550 pre-coordinated concepts is proposed. A diabetes diagnosis is chosen as a case study of our proposed framework. SCT is used to provide a pre coordination concept coverage of ~75% for diabetes diagnosis terms. Whereas, the uncovered concepts in SCT are proposed. The resulting OWL2 ontology will be used as domain knowledge representation in diabetes diagnosis CBR systems. The proposed framework is tested by using 60 real cases.
ISSN: 1319-1578

عناصر مشابهة


© 2024 المنظومة. جميع الحقوق محفوظة.