Marcinkevics Ricards, Reis Wolfertstetter Patricia, Wellmann Sven, Knorr Christian, Vogt Julia E
Department of Computer Science, ETH Zurich, Zurich, Switzerland.
Department of Pediatric Surgery and Pediatric Orthopedics, Hospital St. Hedwig of the Order of St. John of God, University Children's Hospital Regensburg (KUNO), Regensburg, Germany.
Front Pediatr. 2021 Apr 29;9:662183. doi: 10.3389/fped.2021.662183. eCollection 2021.
Given the absence of consolidated and standardized international guidelines for managing pediatric appendicitis and the few strictly data-driven studies in this specific, we investigated the use of machine learning (ML) classifiers for predicting the diagnosis, management and severity of appendicitis in children. Predictive models were developed and validated on a dataset acquired from 430 children and adolescents aged 0-18 years, based on a range of information encompassing history, clinical examination, laboratory parameters, and abdominal ultrasonography. Logistic regression, random forests, and gradient boosting machines were used for predicting the three target variables. A random forest classifier achieved areas under the precision-recall curve of 0.94, 0.92, and 0.70, respectively, for the diagnosis, management, and severity of appendicitis. We identified smaller subsets of 6, 17, and 18 predictors for each of targets that sufficed to achieve the same performance as the model based on the full set of 38 variables. We used these findings to develop the user-friendly online Appendicitis Prediction Tool for children with suspected appendicitis. This pilot study considered the most extensive set of predictor and target variables to date and is the first to simultaneously predict all three targets in children: diagnosis, management, and severity. Moreover, this study presents the first ML model for appendicitis that was deployed as an open access easy-to-use online tool. ML algorithms help to overcome the diagnostic and management challenges posed by appendicitis in children and pave the way toward a more personalized approach to medical decision-making. Further validation studies are needed to develop a finished clinical decision support system.
鉴于缺乏用于管理小儿阑尾炎的统一和标准化国际指南，且针对这一特定领域的严格数据驱动研究较少，我们研究了使用机器学习（ML）分类器来预测儿童阑尾炎的诊断、治疗和严重程度。基于一系列包括病史、临床检查、实验室参数和腹部超声检查的信息，在一个从430名0至18岁儿童和青少年获取的数据集上开发并验证了预测模型。使用逻辑回归、随机森林和梯度提升机来预测三个目标变量。对于阑尾炎的诊断、治疗和严重程度，随机森林分类器在精确召回曲线下的面积分别为0.94、0.92和0.70。我们为每个目标确定了6、17和18个预测因子的较小子集，这些子集足以实现与基于38个变量的完整模型相同的性能。我们利用这些发现为疑似阑尾炎儿童开发了用户友好的在线阑尾炎预测工具。这项初步研究考虑了迄今为止最广泛的预测因子和目标变量集，并且是首次同时预测儿童的所有三个目标：诊断、治疗和严重程度。此外，本研究展示了首个作为开放获取的易于使用的在线工具部署的阑尾炎ML模型。ML算法有助于克服小儿阑尾炎带来的诊断和管理挑战，并为更个性化的医疗决策方法铺平道路。需要进一步的验证研究来开发一个完善的临床决策支持系统。
