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.

Graphical abstract

中文翻译：

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使用基于统计的模糊认知图和人工神经网络的集成方法预测住院时间

慢性阻塞性肺疾病 (COPD) 是一种全球负担，据估计到 2030 年将成为全球第三大死亡原因。 COPD 的经济负担不断增加，因为它不是一种可治愈的疾病。这些条件使 COPD 成为医学人工智能 (AI) 技术的重要研究领域。在这项研究中，提出了一种基于统计的模糊认知图 (SBFCM) 和人工神经网络 (ANN) 的综合方法来预测 COPD 患者的住院时间，这些患者因急性加重入院。开发了 SBFCM 方法来确定 ANN 模型的输入变量。SBFCM 进行统计分析，为专家准备初步信息，然后据此收集专家意见，定义系统的概念图。SBFCM 和 ANN 方法的集成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力量。构建医疗决策支持框架，以更好地预测住院时间和更有效的医院管理。

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