以多重測量案例式推理法建構之知識分享平台: 以肝癌為例

Abstract

臨床資料 (clinical data) 含有許多有用的醫療相關資訊，如果能夠分享其中蘊含的資訊，無論是對於病患或是醫師都會有所助益。但是臨床資會隨著病人回診檢驗，而有所增加或改變，因此可能會有多重測量 (multiple measurements) 而造成難以分析病人全部臨床資料的問題。此研究基於案例式推理 (case-based reasoning, CBR) 架構，提出多重測量案例式推理法 (multiple measurements case-based reasoning, MMsCBR)，可在多重測量的情況下，尋找相似的病患，最後將結果以統計圖表呈現。此研究可提供檢索相似病患的服務給其他有需要的使用者，例如病患或是醫療人員以供參考。

Much valuable medical information is contained in clinical data, and it is helpful to share medical information in the clinical data. Moreover, with the subsequent consultations of patients, volumes of clinical data are growing and values of biochemistry laboratory items are multiple measured. These multiple measurements clinical data may become another problem when analyzing. This study proposes a practicable model to properly share medical knowledge and appropriately handle the multiple measurements problem. Based on case-based reasoning (CBR) model, we analyze clinical data and then the embedded multiple measurements algorithm can be used to find similar patients in the multiple measurements condition. Finally, the clinical information will be represented by the statistics approaches. The system can be used for users, such as physicians, researchers or patients. They can access the services to retrieve similar cases for references.