DQU-CLIP: Enhanced Multimodal for COVID-19 ICU Patients Survival Prediction using CXR and Clinical Data
Bibliographic record
Abstract
Accurate and timely 90-day survival prediction for critically ill COVID-19 patients is vital to optimize scarce ICU resources, yet single-source models often miss important pathophysiological cues. Emerging studies show that combining complementary modalities can reveal richer prognostic signatures than any modality in isolation. Motivated by this, we present DQU-CLIP, an advanced multimodal deep learning framework designed to overcome this limitation. Utilizing the CoCross dataset (comprising 171 ICU patients), our model integrates chest Xrays (CXRs) via a pre-trained Contrastive Language-Image Pretraining (CLIP) encoder with key clinical features, including Age, Charlson Comorbidity Index (CCI), APACHE II, and SOFA scores, processed by a neural network. DQU-CLIP achieves a robust ROC-AUC of 0.85, significantly outperforming unimodal baselines (CXR-only: 0.78, Clinical-only: 0.72) and competing multimodal approaches. Extensive validation and ablation studies confirm the synergistic benefit of this fusion. Furthermore, interpretability analysis using Grad-CAM identified relevant lung regions in CXRs, while feature importance pinpointed SOFA and APACHE II scores as critical indicators of disease severity. By effectively unifying radiological and clinical evidence, DQU-CLIP provides a more reliable prognostic assessment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.003 | 0.001 |
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".