Citation

Nair, Rekha R. and Babu, Tina and Balusamy, Balamurugan and Khoh, Wee How and Momani, Alaa M. and Zneid, Basem Abu (2026) An Interpretable and Uncertainty-Aware Deep Learning Framework for Early Sepsis Prediction Using SHAP-Enhanced Attention and Continuous-Time Neural Networks. Machine Learning and Knowledge Extraction, 8 (5). p. 129. ISSN 2504-4990

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Abstract

Sepsis is a prominent cause of death in intensive care units, and delayed diagnosis greatly worsens fatal outcomes due to the complex, irregular, and uneven character of clinical time-series data. Hence we proposed an interpretable and uncertainty-aware deep learning architecture that solves data quality, temporal irregularity, and clinical explainability restrictions, which are frequently addressed separately by existing models. The suggested method combines Bidirectional Recurrent Imputation for Time Series (BRITS)-based imputation, hybrid Conditional Tabular Generative Adversarial Network-Synthetic Minority Oversampling Technique (CTGAN-SMOTE) data augmentation, a Temporal Convolutional Networks (TCN)-Attention architecture, and continuous-time neural Ordinary Differential Equations (ODEs), along with SHapley Additive exPlanations (SHAP)-based feature attribution and uncertainty quantification. The experimental evaluation on a large ICU dataset reveals greater predictive accuracy, with an AUROC of 0.926 and accurate early warnings up to six hours before clinical onset, all while maintaining strong interpretability and calibration. The proposed framework demonstrates strong predictive performance and provides early warnings up to six hours before clinical onset, while maintaining interpretability and calibration. While the results are promising, further validation across multiple clinical settings is required to confirm its generalisability and real-world applicability.

Item Type:	Article
Uncontrolled Keywords:	Conditional tabular generative adversarial network
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	05 Jun 2026 01:41
Last Modified:	05 Jun 2026 01:41
URII:	http://shdl.mmu.edu.my/id/eprint/15973

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