Abbaspour Onari Mohsen, Yousefi Samuel, Rabieepour Masome, Alizadeh Azra, Jahangoshai Rezaee Mustafa
Faculty of Industrial Engineering, Urmia University of Technology, Urmia, Iran.
Pulmonary Department, Urmia University of Medical Sciences, Urmia, Iran.
Complex Intell Systems. 2021;7(4):2037-2051. doi: 10.1007/s40747-021-00312-1. Epub 2021 Mar 4.
The main assay tool of COVID-19, as a pandemic, still has significant faults. To ameliorate the current situation, all facilities and tools in this realm should be implemented to encounter this epidemic. The current study has endeavored to propose a self-assessment decision support system (DSS) for distinguishing the severity of the COVID-19 between confirmed cases to optimize the patient care process. For this purpose, a DSS has been developed by the combination of the data-driven Bayesian network (BN) and the Fuzzy Cognitive Map (FCM). First, all of the data are utilized to extract the evidence-based paired (EBP) relationships between symptoms and symptoms' impact probability. Then, the results are evaluated in both independent and combined scenarios. After categorizing data in the triple severity levels by self-organizing map, the EBP relationships between symptoms are extracted by BN, and their significance is achieved and ranked by FCM. The results show that the most common symptoms necessarily do not have the key role in distinguishing the severity of the COVID-19, and extracting the EBP relationships could have better insight into the severity of the disease.
作为一种大流行病，新冠病毒病的主要检测工具仍存在重大缺陷。为改善当前状况，该领域的所有设施和工具都应投入使用以应对这一疫情。当前研究致力于提出一种自我评估决策支持系统（DSS），用于区分新冠病毒病确诊病例的严重程度，以优化患者护理流程。为此，通过将数据驱动的贝叶斯网络（BN）和模糊认知图（FCM）相结合，开发了一种决策支持系统。首先，利用所有数据提取症状与症状影响概率之间的循证配对（EBP）关系。然后，在独立和组合场景中对结果进行评估。通过自组织映射将数据分类为三个严重程度级别后，利用贝叶斯网络提取症状之间的EBP关系，并通过模糊认知图确定其重要性并进行排序。结果表明，最常见的症状不一定在区分新冠病毒病严重程度方面起关键作用，提取EBP关系可以更好地洞察疾病的严重程度。
