Department of Infectious Diseases, Chifeng Municipal Hospital, Chifeng Clinical Medical School of Inner Mongolia Medical University, Chifeng 024000, China.
Department of Neurosurgery, Chifeng Municipal Hospital, Chifeng Clinical Medical School of Inner Mongolia Medical University, Chifeng 024000, China.
Comput Math Methods Med. 2022 Mar 4;2022:7003272. doi: 10.1155/2022/7003272. eCollection 2022.
This study was to conduct a model based on the broad learning system (BLS) for predicting the 28-day mortality of patients hospitalized with community-acquired pneumonia (CAP). A total of 1,210 eligible CAP cases from Chifeng Municipal Hospital were finally included in this retrospective case-control study. Random forest (RF) and an eXtreme Gradient Boosting (XGB) models were used to develop the prediction models. The data features extracted from BLS are utilized in RF and XGB models to predict the 28-day mortality of CAP patients, which established two integrated models BLS-RF and BLS-XGB. Our results showed the integrated model BLS-XGB as an efficient broad learning system (BLS) for predicting the death risk of patients, which not only performed better than the two basic models but also performed better than the integrated model BLS-RF and two well-known deep learning systems-deep neural network (DNN) and convolutional neural network (CNN). In conclusion, BLS-XGB may be recommended as an efficient model for predicting the 28-day mortality of CAP patients after hospital admission.
本研究旨在建立基于广泛学习系统（BLS）的模型，预测因社区获得性肺炎（CAP）住院患者的 28 天死亡率。最终，本回顾性病例对照研究纳入了赤峰市医院的 1210 例符合条件的 CAP 病例。本研究使用随机森林（RF）和极端梯度提升（XGB）模型来开发预测模型。从 BLS 中提取的数据特征被用于 RF 和 XGB 模型来预测 CAP 患者的 28 天死亡率，由此建立了两个集成模型 BLS-RF 和 BLS-XGB。我们的研究结果表明，集成模型 BLS-XGB 是一种有效的广泛学习系统（BLS），可用于预测患者的死亡风险，其不仅优于两个基本模型，而且优于集成模型 BLS-RF 和两个著名的深度学习系统——深度神经网络（DNN）和卷积神经网络（CNN）。总之，BLS-XGB 可被推荐为一种有效的模型，用于预测 CAP 患者住院后 28 天的死亡率。
