PDF(Pubmed)
Abstract：
UNASSIGNED: Acute abdominal pain (AAP) is a common symptom presented in the emergency department (ED), and it is crucial to have objective and accurate triage. This study aims to develop a machine learning-based prediction model for AAP triage. The goal is to identify triage indicators for critically ill patients and ensure the prompt availability of diagnostic and treatment resources.
UNASSIGNED: In this study, we conducted a retrospective analysis of the medical records of patients admitted to the ED of Wuhan Puren Hospital with acute abdominal pain in 2019. To identify high-risk factors, univariate and multivariate logistic regression analyses were used with thirty-one predictor variables. Evaluation of eight machine learning triage prediction models was conducted using both test and validation cohorts to optimize the AAP triage prediction model.
UNASSIGNED: Eleven clinical indicators with statistical significance (p < 0.05) were identified, and they were found to be associated with the severity of acute abdominal pain. Among the eight machine learning models constructed from the training and test cohorts, the model based on the artificial neural network (ANN) demonstrated the best performance, achieving an accuracy of 0.9792 and an area under the curve (AUC) of 0.9972. Further optimization results indicate that the AUC value of the ANN model could reach 0.9832 by incorporating only seven variables: history of diabetes, history of stroke, pulse, blood pressure, pale appearance, bowel sounds, and location of the pain.
UNASSIGNED: The ANN model is the most effective in predicting the triage of AAP. Furthermore, when only seven variables are considered, including history of diabetes, etc., the model still shows good predictive performance. This is helpful for the rapid clinical triage of AAP patients and the allocation of medical resources.
UNASSIGNED: In this study, we conducted a retrospective analysis of the medical records of patients admitted to the ED of Wuhan Puren Hospital with acute abdominal pain in 2019. To identify high-risk factors, univariate and multivariate logistic regression analyses were used with thirty-one predictor variables. Evaluation of eight machine learning triage prediction models was conducted using both test and validation cohorts to optimize the AAP triage prediction model.
UNASSIGNED: Eleven clinical indicators with statistical significance (p < 0.05) were identified, and they were found to be associated with the severity of acute abdominal pain. Among the eight machine learning models constructed from the training and test cohorts, the model based on the artificial neural network (ANN) demonstrated the best performance, achieving an accuracy of 0.9792 and an area under the curve (AUC) of 0.9972. Further optimization results indicate that the AUC value of the ANN model could reach 0.9832 by incorporating only seven variables: history of diabetes, history of stroke, pulse, blood pressure, pale appearance, bowel sounds, and location of the pain.
UNASSIGNED: The ANN model is the most effective in predicting the triage of AAP. Furthermore, when only seven variables are considered, including history of diabetes, etc., the model still shows good predictive performance. This is helpful for the rapid clinical triage of AAP patients and the allocation of medical resources.
摘要：
■急性腹痛（AAP）是急诊科（ED）的常见症状，客观准确的分诊至关重要。本研究旨在开发一种基于机器学习的AAP分诊预测模型。目标是确定危重病人的分诊指标，并确保及时提供诊断和治疗资源。
■在这项研究中，我们对2019年武汉普仁医院ED收治的急性腹痛患者的病历资料进行回顾性分析.为了识别高风险因素，采用31个预测变量进行单变量和多变量逻辑回归分析.使用测试和验证队列对八个机器学习分诊预测模型进行评估，以优化AAP分诊预测模型。
■确定了11项具有统计学意义（p<0.05）的临床指标，发现它们与急性腹痛的严重程度有关。在从训练和测试队列构建的八个机器学习模型中，基于人工神经网络(ANN)的模型表现出最佳性能，达到0.9792的精度和0.9972的曲线下面积（AUC）。进一步的优化结果表明，通过仅纳入七个变量，ANN模型的AUC值可以达到0.9832：糖尿病史，中风史,脉搏,血压,苍白的外观，肠鸣音,和疼痛的位置。
■ANN模型在预测AAP的分诊方面最有效。此外，当只考虑七个变量时，包括糖尿病史,等。,该模型仍然显示出良好的预测性能。这有助于AAP患者的快速临床分诊和医疗资源的分配。
■在这项研究中，我们对2019年武汉普仁医院ED收治的急性腹痛患者的病历资料进行回顾性分析.为了识别高风险因素，采用31个预测变量进行单变量和多变量逻辑回归分析.使用测试和验证队列对八个机器学习分诊预测模型进行评估，以优化AAP分诊预测模型。
■确定了11项具有统计学意义（p<0.05）的临床指标，发现它们与急性腹痛的严重程度有关。在从训练和测试队列构建的八个机器学习模型中，基于人工神经网络(ANN)的模型表现出最佳性能，达到0.9792的精度和0.9972的曲线下面积（AUC）。进一步的优化结果表明，通过仅纳入七个变量，ANN模型的AUC值可以达到0.9832：糖尿病史，中风史,脉搏,血压,苍白的外观，肠鸣音,和疼痛的位置。
■ANN模型在预测AAP的分诊方面最有效。此外，当只考虑七个变量时，包括糖尿病史,等。,该模型仍然显示出良好的预测性能。这有助于AAP患者的快速临床分诊和医疗资源的分配。
