End to End Autoencoder MLP Framework for Sepsis Prediction
This work addresses early sepsis detection for ICU patients, offering a robust and generalizable solution, though it is incremental as it builds on existing deep learning methods.
The paper tackled sepsis prediction in ICU settings by introducing an end-to-end deep learning framework that integrates an autoencoder for feature extraction with an MLP classifier, achieving accuracies of 74.6%, 80.6%, and 93.5% on three cohorts and outperforming traditional baselines.
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.