基于深度神经网络的重症监护室急性心力衰竭患者死亡事件预测

IF 4.9 2区 医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Jicheng Huang , Yufeng Cai , Xusheng Wu , Xin Huang , Jianwei Liu , Dehua Hu
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引用次数: 0

摘要

背景重症监护病房(ICU)中的急性心力衰竭(AHF)具有病情危重、进展迅速、复杂多变的特点,其病理生理过程涉及多个器官和系统的相互作用。因此很难全面准确地预测院内死亡事件。传统的基于统计学和机器学习的分析方法存在模型性能不足、先验依赖性导致准确性差、难以充分考虑多种危险因素之间的复杂关系等问题。因此,将深度神经网络(DNN)技术应用于特定场景,预测重症监护下 AHF 患者的死亡事件已成为研究前沿。采用基于电子病历数据挖掘的深度神经网络模型--背向传播神经网络(BPNN)和递归神经网络(RNN),研究重症监护下AHF患者的院内死亡事件判断任务。此外,我们还构建了多个单一机器学习模型和集合学习模型进行对比实验。此外,我们还通过修改深度神经网络模型的分类阈值实现了各种最优性能组合,以满足重症监护室的不同实际需求。最后,我们利用 SHapley Additive exPlanations(SHAP)建立了一个可解释的深度模型,从全局和局部解释的角度挖掘出对每位患者最有影响力的病历特征。结果在该场景下,深度神经网络模型的性能优于单一机器学习模型和集合学习模型,获得了最高的准确率、精确率、召回率、F1 值和 ROC 曲线下面积,分别可达 0.949、0.925、0.983、0.953 和 0.987。SHAP值分析表明,ICU评分(APSIII、OASIS、SOFA)与院内死亡事件的发生显著相关。此外,ICU 评分是最具预测性的特征,这意味着在模型的决策过程中,ICU 评分能提供最关键的信息,对影响急性心力衰竭患者的院内死亡率做出最大的积极或消极贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of mortality events of patients with acute heart failure in intensive care unit based on deep neural network

Background

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.

Methods

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.

Results

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.

Conclusions

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.

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来源期刊
Computer methods and programs in biomedicine
Computer methods and programs in biomedicine 工程技术-工程:生物医学
CiteScore
12.30
自引率
6.60%
发文量
601
审稿时长
135 days
期刊介绍: To encourage the development of formal computing methods, and their application in biomedical research and medical practice, by illustration of fundamental principles in biomedical informatics research; to stimulate basic research into application software design; to report the state of research of biomedical information processing projects; to report new computer methodologies applied in biomedical areas; the eventual distribution of demonstrable software to avoid duplication of effort; to provide a forum for discussion and improvement of existing software; to optimize contact between national organizations and regional user groups by promoting an international exchange of information on formal methods, standards and software in biomedicine. Computer Methods and Programs in Biomedicine covers computing methodology and software systems derived from computing science for implementation in all aspects of biomedical research and medical practice. It is designed to serve: biochemists; biologists; geneticists; immunologists; neuroscientists; pharmacologists; toxicologists; clinicians; epidemiologists; psychiatrists; psychologists; cardiologists; chemists; (radio)physicists; computer scientists; programmers and systems analysts; biomedical, clinical, electrical and other engineers; teachers of medical informatics and users of educational software.
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