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論著名稱:
以機器學習法建立糖尿病營養衛教門診病人血糖變化之預測模型與系統以輔助臨床決策(Using Machine Learning Algorithms to Build a Prediction Model and System for Blood Sugar Change that Assists Nutritional Decision-Making for Diabetes Mellitus)
文獻引用
編著譯者: 劉美媛劉忠峰馬于珊陳佳群
出版日期: 2022.07
刊登出處: 台灣/醫療品質雜誌第 16 卷 第 4 期/22-29 頁
頁  數: 8 點閱次數: 274
下載點數: 32 點 銷售明細: 權利金查詢 變更售價
授 權 者: 財團法人醫院評鑑暨醫療品質策進會 授權者指定不分配權利金給作者)
關 鍵 詞: 糖尿病機器學習預測系統糖化血色素併發症營養衛教
中文摘要: 目的:全球糖尿病盛行率仍持續成長,以機器學習技術建立有效預測糖尿病個案之長期血糖變化模型,並實作成系統,提供予病人營養介入輔助決策之參考有其必要性。
材料與方法:數據來源為奇美醫療體系三院區門診營養衛教系統 2007 至 2019 年加入糖尿病試辦計畫之成年病人就診營養衛教紀錄,依文獻與專業經驗選擇 20 個特徵變數,以多種機器學習演算法建立「預測一年後病人糖化血色素是否改善達 7%以上」之模型。最後挑選最佳模型(Area Under the Curve[AUC] 最高者)實作成預測系統以供臨床使用。
結果:各機器學習法建立之模型精確度在 0.735~0.749 間,其中支持向量機法之敏感性達 0.757、特異性0.739、AUC 值 0.828,為最佳模型。我們將預測系統提供給 3 位營養師試用,均獲得正面的肯定,認為此系統對糖尿病營養諮詢衛教非常有幫助。
結論:以機器學習法建立之預測模型具有優異的品質,為糖尿病營養諮詢衛教提供非常有前景的方法,可作為臨床疾病照護及飲食衛教介入之有效參考,使病人維持良好之長期血糖控制,減少糖尿病引發合併症發生率,有助於提升醫療品質與促進醫病共享決策。
英文關鍵詞: diabetes mellitus (DM)machine learningprediction systemglycosylated hemoglobin (HbA1c)diabetic-related complications and deathsnutrition education
英文摘要: Purpose: The prevalence of diabetes mellitus (DM) continues to increase worldwide. We built a machine learning model and developed a prediction system that is based on an optimal model to effectively predict blood sugar changes in patients with diabetes. Our findings contribute to the implementation of long-term patient nutrition interventions.
Methods: Data of outpatients with type 2 DM who were 20 years or older and underwent nutrition education under a diabetes pay-for-performance program were obtained from the Nutrition and Health System Database of the outpatient clinic of the Chi Mei Hospital network; the data spanned the years from 2007 to 2019. On the basis of literature findings and professional experience, 20 characteristic variables and multiple machine learning algorithms were applied to build a model to predict whether the glycosylated hemoglobin (HbA1c) of the outpatients improved by more than 7% after 1 year. The optimal model (model with the highest area under the curve [AUC]) was selected and used to develop a prediction system for use in clinical settings.
Results: The accuracy levels of the developed models ranged from 0.735 to 0.749; the supportvector- machine model with a sensitivity of 0.757, a specificity of 0.739, and an AUC of 0.828 was the optimal prediction model. The prediction system was tested by three dietitians, who affirmed its usefulness for diabetes meal planning and patient health education.
Conclusion: The prediction model based on machine learning algorithms performed excellently, and it is a promising tool for diabetes meal planning and patient health education. It is also an effective supporting tool for clinical disease care and dietary health education interventions. We believe that the model can help patients maintain favorable long-term blood sugar control, reduce their incidence of diabetes-related complications, improve the quality of medical care and promote shared decisionmaking.
目  次: 前言
研究方法
一、研究設計
二、模型結果變數(Outcome Variable)定義
三、特徵變數與特徵選擇
四、資料處理與模型建立
五、預測系統實作與試用
結果
一、收案方式
二、個案基本資料及生活習慣分析
三、血液生化值結果分析
四、AI 預測模型分析
五、預測系統使用評估
討論
結論
參考文獻
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相關論著:
劉美媛、劉忠峰、馬于珊、陳佳群,以機器學習法建立糖尿病營養衛教門診病人血糖變化之預測模型與系統以輔助臨床決策,醫療品質雜誌,第 16 卷 第 4 期,22-29 頁,2022年07月。
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