多年來，糖尿病已經成為在台灣的十大死因排行榜中死亡率攀升最快的死因，由於糖尿病患者在初期的時候，常常會因為症狀不容易察覺而錯失病情控制與治療的最佳時機，所以一旦罹患了糖尿病就必須進行長期的治療與控制，對政府的醫療成本也造成了龐大的負擔，若患者於日常生活中就可以進行自我的監測，對日常的血糖值進行控管，可以有助於降低糖尿病的併發症發生。因為現今市售的血糖機大多數僅只有單向血糖檢測功能，沒有血糖值的異常偵測以及與患者提供互動的功能，本論文希望透過醫學資料與相關文獻的收集，以糖尿病為例，將所蒐集到的資料進行結構化並藉由整理出的判斷規則及流程，建立基於規則引擎之專家系統，使血糖機與血糖自我管理系統可使用規則引擎進行血糖異常偵測的判斷，期望能對未來血糖機的功能提升能有幫助，甚至可以發展一套糖尿病自我檢測的系統來達到早期控制的目標，將有助於降低患者的人數以及降低醫療的成本。 Over the years, diabetes has become the cause with the fastest rise in mortality rates among the ten leading causes of death in Taiwan. In the early onset, diabetic patients often miss the best time for disease control and treatment as symptoms are not easily detected. Therefore, once a person contracts diabetes, the person has to undergo long-term treatment and control, thus the resulting in high medical costs that burden the government. If patients can self-monitor their blood sugar in daily life, they will be able to reduce complications of diabetes. Since most blood glucose meters available in the market only feature one-way blood sugar testing, without abnormal blood sugar value detection and patient interaction functions, this paper is intended to structuralize data collected through medical data and related literatures and using diabetes as an example. Through determination rules and processes compiled, a rules engine experiment system was setup for blood glucose meters and glucose self-management system to determine abnormal glucose detected through the rules engine. It is expected to contribute to the enhancement of future blood glucose meter functions as well as the development of a diabetes self-test system that helps achieve the goal of early control, thereby decreasing diabetic patients and medical costs.
