The First Affiliated Hospital, Anhui University of Chinese Medicine, Hefei, China.
School of Information Engineering, Anhui University of Chinese Medicine, Hefei, China.
Sci Rep. 2024 Jul 16;14(1):16473. doi: 10.1038/s41598-024-67453-4.
Acute appendicitis is a typical surgical emergency worldwide and one of the common causes of surgical acute abdomen in the elderly. Accurately diagnosing and differentiating acute appendicitis can assist clinicians in formulating a scientific and reasonable treatment plan and providing high-quality medical services for the elderly. In this study, we validated and analyzed the different performances of various machine learning models based on the analysis of clinical data, so as to construct a simple, fast, and accurate estimation method for the diagnosis of early acute appendicitis. The dataset of this paper was obtained from the medical data of elderly patients with acute appendicitis attending the First Affiliated Hospital of Anhui University of Chinese Medicine from January 2012 to January 2022, including 196 males (60.87%) and 126 females (39.13%), including 103 (31.99%) patients with complicated appendicitis and 219 (68.01%) patients with uncomplicated appendicitis. By comparing and analyzing the prediction results of the models implemented by nine different machine learning techniques (LR, CART, RF, SVM, Bayes, KNN, NN, FDA, and GBM), we found that the GBM algorithm gave the optimal results and that sensitivity, specificity, PPV, NPV, precision, recall, F1 and brier are 0.9167, 0.9739, 0.9429, 0.9613, 0.9429, 0.9167, 0.9296, and 0.05649, respectively. The GBM model prediction results are interpreted using the SHAP technology framework. Calibration and Decision curve analysis also show that the machine learning model proposed in this paper has some clinical and economic benefits. Finally, we developed the Shiny application for complicated appendicitis diagnosis to assist clinicians in quickly and effectively recognizing patients with complicated appendicitis (CA) and uncomplicated appendicitis (UA), and to formulate a more reasonable and scientific clinical plan for acute appendicitis patient population promptly.
急性阑尾炎是一种全球范围内的典型外科急症，也是老年人外科急腹症的常见原因之一。准确诊断和鉴别急性阑尾炎可以帮助临床医生制定科学合理的治疗方案，为老年人提供高质量的医疗服务。在本研究中，我们基于临床数据分析验证和分析了各种机器学习模型的不同性能，以构建一种简单、快速、准确的早期急性阑尾炎诊断估计方法。本文的数据集中的老年阑尾炎患者的数据来自于安徽中医药大学第一附属医院从 2012 年 1 月到 2022 年 1 月的医疗记录，包括 196 名男性（60.87%）和 126 名女性（39.13%），其中 103 名（31.99%）为复杂性阑尾炎患者，219 名（68.01%）为非复杂性阑尾炎患者。通过比较和分析由九种不同机器学习技术（LR、CART、RF、SVM、贝叶斯、KNN、NN、FDA 和 GBM）实现的模型的预测结果，我们发现 GBM 算法给出了最佳结果，其灵敏度、特异性、PPV、NPV、精度、召回率、F1 和 brier 分别为 0.9167、0.9739、0.9429、0.9613、0.9429、0.9167、0.9296 和 0.05649。使用 SHAP 技术框架对 GBM 模型的预测结果进行解释。校准和决策曲线分析也表明，本文提出的机器学习模型具有一定的临床和经济效益。最后，我们开发了用于复杂性阑尾炎诊断的 Shiny 应用程序，以帮助临床医生快速有效地识别复杂性阑尾炎（CA）和非复杂性阑尾炎（UA）患者，并为急性阑尾炎患者群体迅速制定更合理、更科学的临床方案。
