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題 名 | Clinical Applications of Artificial Intelligence to Forecast Target Range of Radioimmunometric Intact Parathyroid Hormone in Hemodialysis Patients=預測血液透析病患放射免疫量測完整副甲狀腺素之目標範圍:人工智慧臨床應用 |
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作 者 | 邱建勳; 黃世學; 胡宗明; 陳彥宇; 李友專; 王昱豐; | 書刊名 | 核子醫學雜誌 |
卷 期 | 19:3 民95.09 |
頁 次 | 頁149-159 |
分類號 | 415.816 |
關鍵詞 | 人工智慧; 血液透析; 簡易貝式分類器; 類神經網路; 副甲狀腺素; Artificial intelligence; Hemodialysis; Naive bayes classifier; Neural network; Parathyroid hormone; |
語 文 | 英文(English) |
中文摘要 | 背景:在臨床醫學研究中,人工智慧逐漸用來探勘關於生物本質之預測因子與結果的關係。本研究應用兩種人工智慧模型-人工類神經網路與簡易貝氏分類器-預測血流透析病患血漿完整副甲狀線素濃度之目標範圍。 方法:本研究包括130位穩定之血液透析病患。預測因子包括人口統計資料、相關疾病、各種血液生化檢驗、鈣磷乘積與運鐵蛋白飽和度。根據腎臟疾病結果與品質臨床指引,建議使用放射免疫分析來量測透析病患之血漿完整副甲狀腺素濃度。如果血液透析病患之血漿完整則甲狀腺素濃度介於150ng/mL與300ng/mL,表示達到標準範圍,此為目標組;血漿完整副甲狀腺素濃度小於150ng/mL或大於300ng/mL,表示未達標準範圍,則為非目標組。達到目標組與否,則為人工智慧模型之二元相依變數。另外,使用鐮刀式交叉驗證之反覆取樣技術,可以避免模型不易普遍化的問題。為了比較人工類神經網路與簡易貝氏分類器的效能,必須同時使用接收器運作指標曲線下面積與適合度檢定兩種統計量作為鑑別與校正的比較。 結果:人工類神經網路與簡易貝氏分類器的接收器運作指標曲線下面積分別是0.90±0.06與0.62±0.08,因此人工類神經網路的鑑別能力較為優異(P<0.01)。而兩者的適合度統計量分別是6.88(P=0.08)與6.97(P=0.07),因為人工類神經網路有比較低的適合度與比較高的P值,所以人工類神經網路擁有比較好的適合度。 結論:人工類神經網路可以用來預測血液透析病患血漿完整副甲狀腺素濃度的目標範圍,是一種具有發展潛力的預測工具。 |
英文摘要 | Background: Application of artificial intelligence to predict and explore potential relationship between predictors and outcome in biologic nature has been increasingly used in many clinical scenarios. The purpose of this study was to apply and validate artificial neural network (ANN) and naive Bayes classifier (NBC), two models of artificial intelligence, in predicting the target range of plasma intact parathyroid hormone (iPTH) concentration for hemodialysis patients. Methods: The study population included 130 stable hemodialysis patients. The predictors consisted of demographic characteristics (gender, age), associated diseases (diabetes, hypertension), and blood biochemistries (hemoglobin, protein, albumin, calcium, phosphorus, alkaline phosphatase, and ferritin), calcium-phosphorus product, and transferrin saturation values. Plasma iPTH concentration measured by radioimmunometric assay was the dichotomous outcome variable, either target group (150 ng/L≤iPTH≤300 ng/L) or non-target group (iPTH < 150 ng/L or iPTH > 300ng/L) on the basis of Kidney Disease Outcomes Quality Initiative guidelines. The leave-one-out cross validation was employed to surmount the generalization problem caused by a small amount of study population. To compare the performance of the ANN and NBC models, discrimination was evaluated using the area under the receiver operating characteristic curve (AUC) and calibration was estimated using the Hosmer-Lemeshow goodness-of-fit statistic (H-statistic). Results: Pairwise comparison of each AUC showed that the ANN model significantly outperformed the NBC model (AUC=0.90±0.06 vs. 0.62±0.08, P<0.01). The H-statistic values of the ANN and NBC models were 6.88 (P=0.08) and 6.97 (P=0.07), respectively. The ANN model with a lower H-statistic and a higher P value than the NBC model was associated with a better fit. Conclusion: The ANN model could serve as a promising tool to forecast the target range of plasma iPTH concentration in hemodialysis patients. |
本系統中英文摘要資訊取自各篇刊載內容。