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

邱建勳(Jainn-Shiun Chiu) ； 黃世學(Shih-Hsueh Huang) ； 胡宗明(Tsung-Ming Hu) ； 陳彥宇(Yen-Yu Chen) ； 李友專(Yu-Chuan Li) ； 王昱豐(Yuh-Feng Wang)

《核子醫學雜誌》 19卷3期 (2006/09) Pp. 149-159

https://doi.org/10.6332/ANMS.1903.004

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.

關鍵字

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

並列摘要

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.

並列關鍵字

artificial intelligence ； hemodialysis ； naive Bayes classifier ； neural network ； parathyroid hormone