Mamba-Bi-LSTM with SHAP-Guided Iterative Refinement for Multimodal ARDS Diagnosis: A Dual-System Framework.

Overview

1. Department of Engineering Science, University of Oxford, Parks Road, Oxford OX1 3PJ, UK
2. School of Electronics, Peking University, Science Building No. 5, Yiheyuan Lu, Haidian District, Beijing 100871, China; (F.L.); (J.X.)

Abstract

Acute respiratory distress syndrome (ARDS) is associated with mortality rates up to 46% and remains challenging to diagnose early due to overlapping clinical presentations. We propose a dual-system framework for multimodal ARDS diagnosis that integrates a Mamba-Bi-LSTM primary discrimination system with a TreeSHAP-based verification system whose attribution outputs iteratively refine the primary system’s feature selection gate. The primary system processes heterogeneous clinical inputs—ventilator parameters, blood gas indices, chest imaging, and EEG signals—through a selective state-space Mamba module and bidirectional LSTM layers. The verification system applies TreeSHAP attribution to independently cross-validate primary outputs, provide clinically interpretable evidence, and supply ℓ1-normalised attribution vectors that directly modulate the Mamba feature selection gate weights during offline refinement. A confidence-and-consistency decision mechanism governs final output, and high-confidence predictions are incorporated as curriculum-filtered signals to iteratively recalibrate both systems through a confidence-gated offline refinement protocol. Evaluated on 3742 held-out patients from MIMIC-IV (internal test) and 2594 patients from the eICU Collaborative Research Database across 208 US hospitals (external validation), the complete system achieves 92.8% accuracy and an F1 score of 0.889 after offline iterative recalibration on the internal test set, with 91.6% accuracy and F1 of 0.871 on external validation, extending early warning time from 5.2 to 9.7 h. The P/F ratio consistently ranks as the top predictive feature in alignment with the Berlin definition. Ablation experiments confirm that EEG integration independently contributes a 2.7 percentage point accuracy gain and a 1.9-h extension of the warning window (McNemar χ2=27.0, p<0.001). All performance improvements over single-modality baselines and over existing methods are statistically significant (p<0.001, Bonferroni-corrected). End-to-end processing latency of 350 ms per case is compatible with real-time ICU deployment.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data Availability Statement

MIMIC-IV, MIMIC-III, and eICU Collaborative Research Database data are publicly available through PhysioNet (https://physionet.org) under credentialed access. Source code is available upon reasonable request to the corresponding author.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 9 keywords, 2 funders, 32 references.

Cite

This paper

Chen, M., Luo, F., Xie, J., & Ren, Q. (2026). Mamba-Bi-LSTM with SHAP-Guided Iterative Refinement for Multimodal ARDS Diagnosis: A Dual-System Framework. Bioengineering (Basel, Switzerland), 13(7), 794. https://doi.org/10.3390/bioengineering13070794

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