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耳鼻喉科的疾病知識本體架構擷取系統之研製 = A Study and Implementation of a Disease Knowledge Ontology Acquisition System in Otolaryngology

唐國泰 國立臺南大學 數位學習科技學系碩士班 2022[民111]

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  • 題名:
    耳鼻喉科的疾病知識本體架構擷取系統之研製 = A Study and Implementation of a Disease Knowledge Ontology Acquisition System in Otolaryngology
  • 著者: 唐國泰
  • 國立臺南大學 數位學習科技學系碩士班
  • 主題: 臨床思維能力 醫學本體知識 案例式推論 方格法 資料探勘 耳鼻喉科; Clinical Thinking Competence Medical Ontology Case-based Reasoning Repertory Grid Data Mining Otolaryngology
  • 描述: 現今醫學教育強調[臨床思維能力]的培養，但具臨床專業的醫師通常需要長時間的培育，因此應用資訊科技來輔助醫療教育已成為現今趨勢。然而，不同的醫師可能因臨床經驗而具有不同的臨床知識與決策看法，因此如何有效擷取多位專業醫師的臨床知識來進行彙整，以提供完整的臨床疾病知識以輔助學習與相關應用，便是本研究的主要目的。所以，本研究以耳鼻喉科為目標領域，提出多專家彙整式病症知識架構(Multiple Experts Aggregated Disease-Symptom Ontology, MEA-DSO)建立機制，並基於MEA-DSO開發疾病知識本體架構擷取系統，包含: (1)知識擷取機制: 透過案例式推論(Case-based Reasoning)與方格法(Repertory Grid)技術來讓多位醫師專家能分散式地提供與編輯疾病與病症和關聯定義，(2)知識分析機制: 透過資料分析與探勘技術來擷取各疾病與症狀間之關聯性，(3)知識建立機制: 透過決策樹與Ontology技術來建構彙整後的病症知識架構，以提供作為(4)知識應用: 適性測驗與病症學習之知識來源使用。
    At present, medical education emphasizes on the cultivation and training of clinical thinking competence, but cultivating a qualified physician is time-consuming. Accordingly, the application of information technology to assist the medical education has become a trend. However, different physicians may have different clinical knowledge and decision-making views due to their clinical experience. Therefore, how to extract the clinical knowledge of multiple qualified physicians and build a complete clinical disease knowledge to assist medical learning and application is the main goal in this thesis. Therefore, in the otolaryngology domain, this thesis develops a Disease Knowledge Ontology Acquisition System based on the proposed Multiple Experts Aggregated Disease-Symptom Ontology (MEA-DSO) construction scheme. MEA-DSO consists of (1) Knowledge Acquisition: uses Case-based Reasoning and Repertory Grid techniques to enable multiple physician specialists to provide the symptoms and association definition of a disease, (2) Knowledge Analysis: uses the data analysis and data mining techniques to extract the association between diseases and symptoms, (3) Knowledge Construction: uses decision and ontology techniques to construct the aggregated Disease-Symptom Ontology, and (4) Knowledge Application: uses the MEA-DSO to develop the applications of adaptive testing and disease learning.
  • 出版者: 碩士論文--國立臺南大學數位學習科技學系碩博士班.
  • 建立日期: 2022[民111]
  • 格式: 56葉 : 圖 ; 30公分..
  • 語言: 中文
  • 資源來源: NUTN ALEPH
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