用自编码器自动提取特征，提升重症监护室脓毒症早期预测准确率。
End to End Autoencoder MLP Framework for Sepsis Prediction
- 端到端框架结合自编码器与多层感知机，自动处理时间序列数据。
- 在三个ICU队列中准确率达74.6%至93.5%，优于传统模型。
- 适合临床实时监测，对不完整数据有强鲁棒性，可推广至不同医院。
脓毒症是一种危及生命的病症，需在重症监护环境中及时发现。传统机器学习方法如朴素贝叶斯、支持向量机（SVM）、随机森林和XGBoost常依赖人工特征工程，难以处理电子健康记录中常见的不规则、不完整时间序列数据。本文提出一种端到端深度学习框架，结合无监督自编码器进行自动特征提取，以及多层感知机分类器实现二分类脓毒症风险预测。为提升临床适用性，采用定制化下采样策略提取高信息密度片段，并引入非重叠动态滑动窗口机制实现实时推理。预处理后的时间序列数据被表示为固定维度向量并显式标注缺失值，有效减少偏差与噪声。在三个ICU队列上验证结果表明，该模型准确率分别为74.6%、80.6%和93.5%，持续优于传统机器学习基线。结果表明该框架在异构ICU环境中具备更优的鲁棒性、泛化能力与临床应用价值。
原文摘要 · Abstract (English)
Sepsis is a life threatening condition that requires timely detection in intensive care settings. Traditional machine learning approaches, including Naive Bayes, Support Vector Machine (SVM), Random Forest, and XGBoost, often rely on manual feature engineering and struggle with irregular, incomplete time-series data commonly present in electronic health records. We introduce an end-to-end deep learning framework integrating an unsupervised autoencoder for automatic feature extraction with a multilayer perceptron classifier for binary sepsis risk prediction. To enhance clinical applicability, we implement a customized down sampling strategy that extracts high information density segments during training and a non-overlapping dynamic sliding window mechanism for real-time inference. Preprocessed time series data are represented as fixed dimension vectors with explicit missingness indicators, mitigating bias and noise. We validate our approach on three ICU cohorts. Our end-to-end model achieves accuracies of 74.6 percent, 80.6 percent, and 93.5 percent, respectively, consistently outperforming traditional machine learning baselines. These results demonstrate the framework's superior robustness, generalizability, and clinical utility for early sepsis detection across heterogeneous ICU environments.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。