XAI-Driven Multimodal Deep Learning for Early Sepsis Prediction in ICU
Bibliographic record
Abstract
Early detection of sepsis in intensive care units (ICUs) remains a critical challenge due to the rapid progression of the condition and the complexity of physiological signals associated with its onset. Advances in artificial intelligence, particularly deep learning, have enabled the development of predictive models capable of identifying early warning signs of sepsis from large-scale clinical datasets. However, many of these models operate as black-box systems, limiting their interpretability and reducing clinical trust. This study presents an explainable artificial intelligence (XAI)-driven multimodal deep learning framework designed to improve early sepsis prediction in ICU environments. The proposed approach integrates multiple healthcare data modalities, including vital signs, laboratory measurements, and electronic health records, to capture complex interactions among clinical variables. In addition to achieving high predictive performance, the framework incorporates explainability techniques that highlight the most influential clinical features contributing to the model’s predictions. The results demonstrate that the multimodal model improves prediction accuracy and enables earlier detection of sepsis compared to traditional machine learning approaches, while also providing transparent insights to support clinical decision-making. The findings highlight the potential of combining multimodal deep learning and explainable AI to enhance patient monitoring systems and assist healthcare professionals in making timely and informed interventions in critical care settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".