PDF(Pubmed)
Abstract：
UNASSIGNED: Biliary atresia is a rare and devastating bile duct disease that occurs during the neonatal period. Timely identification and prompt surgical intervention is critical for improving the outcome. The aim of the study was to develop a new machine learning-based prediction model for the detection of biliary atresia.
UNASSIGNED: Neonates aged <100 days with cholestasis at least once were retrospectively screened in 2 tertiary referral hospitals between 2015 and 2020. Simple demographic data, routine laboratory indices, and imaging findings of ultrasonography and hepatobiliary scintigraphy were used as features in the multivariate analysis. The extreme gradient boosting (XGBoost) framework was used to develop prediction models according to the diagnostic steps.
UNASSIGNED: Among 1605 enrolled neonates with all-cause cholestasis, 145 (9%) were included as having biliary atresia. Direct bilirubin, gamma-glutamyl transpeptidase, abdominal sonography, and hepatobiliary scan were the most impactful features in prediction models. The Step II XGBoost model, consisting of nonimaging inputs, showed excellent discriminatory performance (area under the curve = 0.97). The Step III and IV XGBoost models showed near-perfect performances (area under the curve = 0.998 and 0.999, respectively). In external validation (n = 912 with 118 [12.9%] biliary atresia), XGBoost-based prediction models consistently showed acceptable performances. Utilizing shapley additive explanation values also provided visualized insight and explanation of the contribution of features in detecting biliary atresia. The models were integrated into a web-based diagnostic tool for case-level application.
UNASSIGNED: We introduced a new machine learning-based prediction model for detecting biliary atresia in the largest cohorts of neonatal cholestasis.
UNASSIGNED: Neonates aged <100 days with cholestasis at least once were retrospectively screened in 2 tertiary referral hospitals between 2015 and 2020. Simple demographic data, routine laboratory indices, and imaging findings of ultrasonography and hepatobiliary scintigraphy were used as features in the multivariate analysis. The extreme gradient boosting (XGBoost) framework was used to develop prediction models according to the diagnostic steps.
UNASSIGNED: Among 1605 enrolled neonates with all-cause cholestasis, 145 (9%) were included as having biliary atresia. Direct bilirubin, gamma-glutamyl transpeptidase, abdominal sonography, and hepatobiliary scan were the most impactful features in prediction models. The Step II XGBoost model, consisting of nonimaging inputs, showed excellent discriminatory performance (area under the curve = 0.97). The Step III and IV XGBoost models showed near-perfect performances (area under the curve = 0.998 and 0.999, respectively). In external validation (n = 912 with 118 [12.9%] biliary atresia), XGBoost-based prediction models consistently showed acceptable performances. Utilizing shapley additive explanation values also provided visualized insight and explanation of the contribution of features in detecting biliary atresia. The models were integrated into a web-based diagnostic tool for case-level application.
UNASSIGNED: We introduced a new machine learning-based prediction model for detecting biliary atresia in the largest cohorts of neonatal cholestasis.
摘要：
■胆道闭锁是一种罕见的破坏性胆管疾病，发生在新生儿期。及时识别和及时手术干预对于改善预后至关重要。该研究的目的是开发一种新的基于机器学习的胆道闭锁预测模型。
■在2015年至2020年期间，在2家三级转诊医院对患有胆汁淤积的年龄<100天的新生儿进行了至少一次回顾性筛查。简单的人口统计数据，常规实验室指标，超声和肝胆显像的影像学表现被用作多变量分析的特征。极端梯度提升(XGBoost)框架用于根据诊断步骤开发预测模型。
■在1605名全因胆汁淤积的新生儿中，145（9％）被包括为胆道闭锁。直接胆红素,γ-谷氨酰转肽酶,腹部超声检查,和肝胆扫描是预测模型中最有影响的特征。第二步XGBoost模型，由非成像输入组成，表现出优异的判别性能(曲线下面积=0.97)。步骤III和IVXGBoost模型显示出近乎完美的性能（曲线下面积分别为0.998和0.999）。在外部验证中（n=912，118[12.9%]胆道闭锁），基于XGBoost的预测模型始终显示出可接受的性能。利用shapley加性解释值还提供了可视化的见解，并解释了特征在检测胆道闭锁中的贡献。这些模型已集成到基于Web的诊断工具中，用于案例级别的应用。
■我们引入了一种新的基于机器学习的预测模型，用于在最大的新生儿胆汁淤积队列中检测胆道闭锁。
■在2015年至2020年期间，在2家三级转诊医院对患有胆汁淤积的年龄<100天的新生儿进行了至少一次回顾性筛查。简单的人口统计数据，常规实验室指标，超声和肝胆显像的影像学表现被用作多变量分析的特征。极端梯度提升(XGBoost)框架用于根据诊断步骤开发预测模型。
■在1605名全因胆汁淤积的新生儿中，145（9％）被包括为胆道闭锁。直接胆红素,γ-谷氨酰转肽酶,腹部超声检查,和肝胆扫描是预测模型中最有影响的特征。第二步XGBoost模型，由非成像输入组成，表现出优异的判别性能(曲线下面积=0.97)。步骤III和IVXGBoost模型显示出近乎完美的性能（曲线下面积分别为0.998和0.999）。在外部验证中（n=912，118[12.9%]胆道闭锁），基于XGBoost的预测模型始终显示出可接受的性能。利用shapley加性解释值还提供了可视化的见解，并解释了特征在检测胆道闭锁中的贡献。这些模型已集成到基于Web的诊断工具中，用于案例级别的应用。
■我们引入了一种新的基于机器学习的预测模型，用于在最大的新生儿胆汁淤积队列中检测胆道闭锁。
