López-Canay Julia, Casal-Guisande Manuel, Pinheira Alberto, Golpe Rafael, Comesaña-Campos Alberto, Fernández-García Alberto, Represas-Represas Cristina, Fernández-Villar Alberto
Fundación Pública Galega de Investigación Biomédica Galicia Sur, Hospital Álvaro Cunqueiro, 36312 Vigo, Spain.
NeumoVigo I+i Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, 36312 Vigo, Spain.
Diagnostics (Basel). 2025 Jan 29;15(3):318. doi: 10.3390/diagnostics15030318.
COPD is a chronic disease characterized by frequent exacerbations that require hospitalization, significantly increasing the care burden. In recent years, the use of artificial intelligence-based tools to improve the management of patients with COPD has progressed, but the prediction of readmission has been less explored. In fact, in the state of the art, no models specifically designed to make medium-term readmission predictions (2-3 months after admission) have been found. This work presents a new intelligent clinical decision support system to predict the risk of hospital readmission in 90 days in patients with COPD after an episode of acute exacerbation. The system is structured in two levels: the first one consists of three machine learning algorithms -Random Forest, Naïve Bayes, and Multilayer Perceptron-that operate concurrently to predict the risk of readmission; the second level, an expert system based on a fuzzy inference engine that combines the generated risks, determining the final prediction. The employed database includes more than five hundred patients with demographic, clinical, and social variables. Prior to building the model, the initial dataset was divided into training and test subsets. In order to reduce the high dimensionality of the problem, filter-based feature selection techniques were employed, followed by recursive feature selection supported by the use of the Random Forest algorithm, guaranteeing the usability of the system and its potential integration into the clinical environment. After training the models in the first level, the knowledge base of the expert system was determined on the training data subset using the Wang-Mendel automatic rule generation algorithm. Preliminary results obtained on the test set are promising, with an AUC of approximately 0.8. At the selected cutoff point, a sensitivity of 0.67 and a specificity of 0.75 were achieved. This highlights the system's future potential for the early identification of patients at risk of readmission. For future implementation in clinical practice, an extensive clinical validation process will be required, along with the expansion of the database, which will likely contribute to improving the system's robustness and generalization capacity.
慢性阻塞性肺疾病（COPD）是一种以频繁急性加重需住院治疗为特征的慢性疾病，这显著增加了护理负担。近年来，利用基于人工智能的工具改善COPD患者管理取得了进展，但再入院预测方面的探索较少。事实上，在现有技术水平下，尚未发现专门用于进行中期再入院预测（入院后2 - 3个月）的模型。这项工作提出了一种新的智能临床决策支持系统，用于预测COPD患者急性加重发作后90天内再次入院的风险。该系统分为两个层次：第一个层次由三种机器学习算法——随机森林、朴素贝叶斯和多层感知器——并行运行以预测再入院风险；第二个层次是一个基于模糊推理引擎的专家系统，它结合生成的风险来确定最终预测。所使用的数据库包含五百多名具有人口统计学、临床和社会变量的患者。在构建模型之前，初始数据集被划分为训练子集和测试子集。为了降低问题的高维度，采用了基于滤波器的特征选择技术，随后使用随机森林算法支持的递归特征选择，以确保系统的可用性及其在临床环境中的潜在整合。在对第一层的模型进行训练后，使用王 - 门德尔自动规则生成算法在训练数据子集上确定专家系统的知识库。在测试集上获得的初步结果很有前景，曲线下面积（AUC）约为0.8。在选定的截断点，灵敏度为0.67，特异性为0.75。这突出了该系统在早期识别有再入院风险患者方面的未来潜力。对于未来在临床实践中的实施，将需要广泛的临床验证过程以及数据库的扩展，这可能有助于提高系统的稳健性和泛化能力。
