School of Life Sciences, Central South University, Changsha, China.
Shenzhen Health Development Research and Data Management Center, Shenzhen, China.
Comput Methods Programs Biomed. 2024 Nov;256:108403. doi: 10.1016/j.cmpb.2024.108403. Epub 2024 Aug 30.
Acute heart failure (AHF) in the intensive care unit (ICU) is characterized by its criticality, rapid progression, complex and changeable condition, and its pathophysiological process involves the interaction of multiple organs and systems. This makes it difficult to predict in-hospital mortality events comprehensively and accurately. Traditional analysis methods based on statistics and machine learning suffer from insufficient model performance, poor accuracy caused by prior dependence, and difficulty in adequately considering the complex relationships between multiple risk factors. Therefore, the application of deep neural network (DNN) techniques to the specific scenario, predicting mortality events of patients with AHF under intensive care, has become a research frontier.
This research utilized the MIMIC-IV critical care database as the primary data source and employed the synthetic minority over-sampling technique (SMOTE) to balance the dataset. Deep neural network models-backpropagation neural network (BPNN) and recurrent neural network (RNN), which are based on electronic medical record data mining, were employed to investigate the in-hospital death event judgment task of patients with AHF under intensive care. Additionally, multiple single machine learning models and ensemble learning models were constructed for comparative experiments. Moreover, we achieved various optimal performance combinations by modifying the classification threshold of deep neural network models to address the diverse real-world requirements in the ICU. Finally, we conducted an interpretable deep model using SHapley Additive exPlanations (SHAP) to uncover the most influential medical record features for each patient from the aspects of global and local interpretation.
In terms of model performance in this scenario, deep neural network models outperform both single machine learning models and ensemble learning models, achieving the highest Accuracy, Precision, Recall, F1 value, and Area under the ROC curve, which can reach 0.949, 0.925, 0.983, 0.953, and 0.987 respectively. SHAP value analysis revealed that the ICU scores (APSIII, OASIS, SOFA) are significantly correlated with the occurrence of in-hospital fatal events.
Our study underscores that DNN-based mortality event classifier offers a novel intelligent approach for forecasting and assessing the prognosis of AHF patients in the ICU. Additionally, the ICU scores stand out as the most predictive features, which implies that in the decision-making process of the models, ICU scores can provide the most crucial information, making the greatest positive or negative contribution to influence the incidence of in-hospital mortality among patients with acute heart failure.
重症监护病房（ICU）中的急性心力衰竭（AHF）以其关键性、快速进展、复杂多变的病情为特征，其病理生理过程涉及多个器官和系统的相互作用。这使得全面准确地预测院内死亡率事件变得困难。基于统计学和机器学习的传统分析方法存在模型性能不足、先验依赖性导致的准确性较差以及难以充分考虑多个危险因素之间复杂关系等问题。因此，将深度学习神经网络（DNN）技术应用于特定场景，预测 ICU 中 AHF 患者的死亡率事件，已成为研究前沿。
本研究以 MIMIC-IV 重症监护数据库为主要数据源，采用合成少数过采样技术（SMOTE）对数据集进行平衡。基于电子病历数据挖掘的深度神经网络模型-反向传播神经网络（BPNN）和递归神经网络（RNN），用于研究 ICU 中 AHF 患者的院内死亡事件判断任务。此外，构建了多个单机器学习模型和集成学习模型进行对比实验。并且，通过修改深度神经网络模型的分类阈值，以满足 ICU 中的各种实际需求，实现了各种最优性能组合。最后，使用 SHapley Additive exPlanations（SHAP）进行可解释性深度模型分析，从全局和局部解释两个方面揭示每个患者最具影响力的病历特征。
在该场景下的模型性能方面，深度神经网络模型优于单机器学习模型和集成学习模型，其准确率、精确率、召回率、F1 值和 ROC 曲线下面积（AUC）最高，分别可达 0.949、0.925、0.983、0.953 和 0.987。SHAP 值分析表明，重症监护评分（APSIII、OASIS、SOFA）与院内死亡事件的发生显著相关。
本研究表明，基于 DNN 的死亡率事件分类器为预测和评估 ICU 中 AHF 患者的预后提供了一种新的智能方法。此外，重症监护评分是最具预测性的特征，这意味着在模型的决策过程中，重症监护评分可以提供最重要的信息，对影响急性心力衰竭患者院内死亡率的发生有最大的积极或消极贡献。
