Modeling of Noncommunicable Diseases Research Center, Hamadan University of Medical Sciences, Hamadan, Iran.
Emergency Department, Besat Hospital, Hamadan University of Medical Sciences, Hamadan, Iran.
Intern Emerg Med. 2024 Nov;19(8):2347-2357. doi: 10.1007/s11739-024-03738-w. Epub 2024 Aug 21.
The diagnosis of abdominal pain in emergency departments is challenging, and appendicitis is a common concern. Atypical symptoms often delay diagnosis. Although the Alvarado score aids in decision-making, its low specificity can lead to unnecessary surgeries. By leveraging machine learning, we aim to enhance diagnostic accuracy by predicting appendicitis and distinguishing it from other causes of abdominal pain in the emergency department. Data were collected from 534 patients who presented with acute abdominal pain. Patient characteristics, laboratory results, and causes of pain were recorded. Machine learning algorithms (support vector classifier, random forest classifier, gradient boosting classifier, and Gaussian naive Bayes) were used to predict the cause of pain. Model calibration was assessed using the Brier score. The mean age was 46.89 (20.3) years, with an almost equal sex distribution (49% male, 51% female). Cholecystitis was the most prevalent outcome (37.07%), followed by appendicitis (25.84%). The Gaussian naive Bayes model exhibited superior performance in terms of accuracy (95.03% 95% CI 90.44-97.83%), sensitivity (87.18% 95% CI 72.57-95.70%), and specificity (97.54% 95% CI 92.98-99.49%), while the random forest model showed a sensitivity of 79.49%, specificity of 96.72%, and accuracy of 92.55%. The gradient boosting algorithm achieved a sensitivity, specificity, and accuracy of 89.74%, 95.90%, and 94.41%, respectively. The support vector classifier demonstrated a sensitivity of 89.74%, specificity of 92.62%, and accuracy of 91.93%. The use of modern machine learning methods aids in the accurate diagnosis of appendicitis.
急诊科腹痛的诊断具有挑战性，阑尾炎是常见的关注点。不典型的症状常常导致诊断延迟。尽管 Alvarado 评分有助于决策，但它的低特异性可能导致不必要的手术。通过利用机器学习，我们旨在通过预测阑尾炎并将其与急诊科其他腹痛原因区分开来，从而提高诊断准确性。数据来自 534 名因急性腹痛就诊的患者。记录了患者特征、实验室结果和疼痛原因。使用机器学习算法（支持向量分类器、随机森林分类器、梯度提升分类器和高斯朴素贝叶斯）来预测疼痛的原因。使用 Brier 评分评估模型校准。平均年龄为 46.89（20.3）岁，性别分布几乎相等（男性占 49%，女性占 51%）。胆囊炎是最常见的结果（37.07%），其次是阑尾炎（25.84%）。高斯朴素贝叶斯模型在准确性（95.03% 95%置信区间 90.44-97.83%）、敏感性（87.18% 95%置信区间 72.57-95.70%）和特异性（97.54% 95%置信区间 92.98-99.49%）方面表现出卓越的性能，而随机森林模型的敏感性为 79.49%，特异性为 96.72%，准确性为 92.55%。梯度提升算法的敏感性、特异性和准确性分别为 89.74%、95.90%和 94.41%。支持向量分类器的敏感性为 89.74%，特异性为 92.62%，准确性为 91.93%。现代机器学习方法的使用有助于阑尾炎的准确诊断。
