Doğan Yunus, Bor Serhat
Department of Computer Engineering, Dokuz Eylül University, Izmir 35390, Türkiye.
Department of Gastroenterology, Ege University Faculty of Medicine, Bornova, Izmir 35100, Türkiye.
Healthcare (Basel). 2023 Jun 17;11(12):1790. doi: 10.3390/healthcare11121790.
Gastroesophageal reflux disease (GERD) is a multidisciplinary disease; therefore, when treating GERD, a large amount of data needs to be monitored and managed.The aim of our study was to develop a novel automation and decision support system for GERD, primarily to automatically determine GERD and its Chicago Classification 3.0 (CC 3.0) phenotypes. However, phenotyping is prone to errors and is not a strategy widely known by physicians, yet it is very important in patient treatment. In our study, the GERD phenotype algorithm was tested on a dataset with 2052 patients and the CC 3.0 algorithm was tested on a dataset with 133 patients. Based on these two algorithms, a system was developed with an artificial intelligence model for distinguishing four phenotypes per patient. When a physician makes a wrong phenotyping decision, the system warns them and provides the correct phenotype. An accuracy of 100% was obtained for both GERD phenotyping and CC 3.0 in these tests. Finally, since the transition to using this developed system in 2017, the annual number of cured patients, around 400 before, has increased to 800. Automatic phenotyping provides convenience in patient care, diagnosis, and treatment management. Thus, the developed system can substantially improve the performance of physicians.
胃食管反流病（GERD）是一种多学科疾病；因此，在治疗GERD时，需要监测和管理大量数据。我们研究的目的是开发一种用于GERD的新型自动化和决策支持系统，主要用于自动确定GERD及其芝加哥分类3.0（CC 3.0）表型。然而，表型分析容易出错，且并非医生广泛知晓的策略，但它在患者治疗中非常重要。在我们的研究中，GERD表型算法在一个包含2052名患者的数据集上进行了测试，CC 3.0算法在一个包含133名患者的数据集上进行了测试。基于这两种算法，开发了一个带有人工智能模型的系统，用于为每位患者区分四种表型。当医生做出错误的表型分析决策时，系统会向他们发出警告并提供正确的表型。在这些测试中，GERD表型分析和CC 3.0的准确率均达到了100%。最后，自2017年开始使用这个开发的系统以来，每年治愈的患者数量从之前的约400例增加到了800例。自动表型分析为患者护理、诊断和治疗管理提供了便利。因此，开发的系统可以显著提高医生的工作表现。
