PDF(Pubmed)
Abstract：
UNASSIGNED: Whether machine learning (ML) can assist in the diagnosis of Crohn\'s disease (CD) and intestinal tuberculosis (ITB) remains to be explored.
UNASSIGNED: We collected clinical data from 241 patients, and 51 parameters were included. Six ML methods were tested, including logistic regression, decision tree, k-nearest neighbor, multinomial NB, multilayer perceptron, and XGBoost. SHAP and LIME were subsequently introduced as interpretability methods. The ML model was tested in a real-world clinical practice and compared with a multidisciplinary team (MDT) meeting.
UNASSIGNED: XGBoost displays the best performance among the six ML models. The diagnostic AUROC and the accuracy of XGBoost were 0.946 and 0.884, respectively. The top three clinical features affecting our ML model\'s result prediction were T-spot, pulmonary tuberculosis, and onset age. The ML model\'s accuracy, sensitivity, and specificity in clinical practice were 0.860, 0.833, and 0.871, respectively. The agreement rate and kappa coefficient of the ML and MDT methods were 90.7% and 0.780, respectively (P<0.001).
UNASSIGNED: We developed an ML model based on XGBoost. The ML model could provide effective and efficient differential diagnoses of ITB and CD with diagnostic bases. The ML model performs well in real-world clinical practice, and the agreement between the ML model and MDT is strong.
UNASSIGNED: We collected clinical data from 241 patients, and 51 parameters were included. Six ML methods were tested, including logistic regression, decision tree, k-nearest neighbor, multinomial NB, multilayer perceptron, and XGBoost. SHAP and LIME were subsequently introduced as interpretability methods. The ML model was tested in a real-world clinical practice and compared with a multidisciplinary team (MDT) meeting.
UNASSIGNED: XGBoost displays the best performance among the six ML models. The diagnostic AUROC and the accuracy of XGBoost were 0.946 and 0.884, respectively. The top three clinical features affecting our ML model\'s result prediction were T-spot, pulmonary tuberculosis, and onset age. The ML model\'s accuracy, sensitivity, and specificity in clinical practice were 0.860, 0.833, and 0.871, respectively. The agreement rate and kappa coefficient of the ML and MDT methods were 90.7% and 0.780, respectively (P<0.001).
UNASSIGNED: We developed an ML model based on XGBoost. The ML model could provide effective and efficient differential diagnoses of ITB and CD with diagnostic bases. The ML model performs well in real-world clinical practice, and the agreement between the ML model and MDT is strong.
摘要：
■机器学习（ML）是否可以帮助诊断克罗恩病（CD）和肠结核（ITB）仍有待探索。
■我们收集了241名患者的临床数据，包括51个参数。测试了六种ML方法，包括逻辑回归，决策树,k-最近邻,多项式NB，多层感知器，XGBoost随后引入SHAP和LIME作为可解释性方法。ML模型在现实世界的临床实践中进行了测试，并与多学科团队（MDT）会议进行了比较。
■XGBoost在六种ML型号中表现最佳。诊断AUROC和XGBoost的准确性分别为0.946和0.884。影响我们ML模型结果预测的前三个临床特征是T点，肺结核,和发病年龄。ML模型的准确性，灵敏度,在临床实践中的特异性分别为0.860、0.833和0.871。ML和MDT方法的符合率和κ系数分别为90.7%和0.780(P<0.001)。
■我们开发了一个基于XGBoost的ML模型。ML模型可以为ITB和CD的有效和高效的鉴别诊断提供诊断依据。ML模型在现实临床实践中表现良好，ML模型和MDT之间的一致性很强。
■我们收集了241名患者的临床数据，包括51个参数。测试了六种ML方法，包括逻辑回归，决策树,k-最近邻,多项式NB，多层感知器，XGBoost随后引入SHAP和LIME作为可解释性方法。ML模型在现实世界的临床实践中进行了测试，并与多学科团队（MDT）会议进行了比较。
■XGBoost在六种ML型号中表现最佳。诊断AUROC和XGBoost的准确性分别为0.946和0.884。影响我们ML模型结果预测的前三个临床特征是T点，肺结核,和发病年龄。ML模型的准确性，灵敏度,在临床实践中的特异性分别为0.860、0.833和0.871。ML和MDT方法的符合率和κ系数分别为90.7%和0.780(P<0.001)。
■我们开发了一个基于XGBoost的ML模型。ML模型可以为ITB和CD的有效和高效的鉴别诊断提供诊断依据。ML模型在现实临床实践中表现良好，ML模型和MDT之间的一致性很强。
