Dogu Elif, Albayrak Y Esra, Tuncay Esin
Industrial Engineering Dept., Galatasaray University, Ciragan Cad. No.: 36, Ortakoy, 34349, Istanbul, Turkey.
Yedikule Chest Diseases & Thoracic Surgery Training & Research Hospital, Belgrad Kapi Yolu Cad. No.: 1 34020 Zeytinburnu, Istanbul, Turkey.
Med Biol Eng Comput. 2021 Mar;59(3):483-496. doi: 10.1007/s11517-021-02327-9. Epub 2021 Feb 5.
Chronic obstructive pulmonary disease (COPD) is a global burden, which is estimated to be the third leading cause of death worldwide by 2030. The economic burden of COPD grows continuously because it is not a curable disease. These conditions make COPD an important research field of artificial intelligence (AI) techniques in medicine. In this study, an integrated approach of the statistical-based fuzzy cognitive maps (SBFCM) and artificial neural networks (ANN) is proposed for predicting length of hospital stay of patients with COPD, who admitted to the hospital with an acute exacerbation. The SBFCM method is developed to determine the input variables of the ANN model. The SBFCM conducts statistical analysis to prepare preliminary information for the experts and then collects expert opinions accordingly, to define a conceptual map of the system. The integration of SBFCM and ANN methods provides both statistical data and expert opinion in the prediction model. In the numerical application, the proposed approach outperformed the conventional approach and other machine learning algorithms with 79.95% accuracy, revealing the power of expert opinion involvement in medical decisions. A medical decision support framework is constructed for better prediction of length of hospital stay and more effective hospital management.
慢性阻塞性肺疾病（COPD）是一项全球性负担，预计到2030年将成为全球第三大死因。由于COPD是一种无法治愈的疾病，其经济负担持续增长。这些情况使得COPD成为医学领域人工智能（AI）技术的一个重要研究方向。在本研究中，提出了一种基于统计的模糊认知图（SBFCM）和人工神经网络（ANN）的综合方法，用于预测因急性加重而住院的COPD患者的住院时间。开发SBFCM方法以确定ANN模型的输入变量。SBFCM进行统计分析以为专家准备初步信息，然后据此收集专家意见，以定义系统的概念图。SBFCM和ANN方法的整合在预测模型中提供了统计数据和专家意见。在数值应用中，所提出的方法以79.95%的准确率优于传统方法和其他机器学习算法，揭示了专家意见参与医疗决策的作用。构建了一个医疗决策支持框架，以更好地预测住院时间并实现更有效的医院管理。
