Multimodal Transformer–Based Electrocardiogram Analysis for Cardiovascular Comorbidity Detection: Model Development and Validation Study
Bibliographic record
Abstract
BACKGROUND: Cardiovascular diseases (CVDs) remain the leading global cause of mortality, yet traditional electrocardiogram (ECG) interpretation suffers from subjective variability and limited sensitivity to complex pathologies. OBJECTIVE: To address these challenges, we propose the Cardiovascular Multimodal Prediction Network (CaMPNet), a Transformer-based multimodal architecture that integrates raw 12-lead ECG waveforms, nine structured machine-measured the electrocardiogram (ECG) features, and demographic data (age and sex) through cross-attention fusion. METHODS: The model was trained on 384,877 records from the MIMIC-IV-ECG database and evaluated across 12 cardiovascular disease labels. To further assess temporal robustness, a temporal external validation was performed using the most recent 10% of the data, withheld chronologically from model development. RESULTS: On the internal test set, the model achieved a mean Area Under the Curve (AUC) of 0.845 and Area Under the Precision-Recall Curve (AUPRC) of 0.489, outperforming the ResNet-ECG baseline (AUC 0.848 but F1 0.152) and all single-modality variants. Subgroup analyses demonstrated consistent performance across demographics (male AUC 0.846 vs female 0.843; youngest quartile 0.884 vs oldest 0.811). CaMPNet retained moderate discriminative ability in temporal external validation with a mean AUC of 0.715 and AUPRC of 0.298, though performance declined due to temporal distribution shifts. Despite this, major disease categories such as atrial fibrillation, heart failure, and normal rhythm maintained high AUCs (> 0.84). Attention-based visualization revealed clinically interpretable patterns (e.g., ST-segment elevations in ST-Segment Elevation Myocardial Infarction), and ablation experiments verified the model's tolerance to missing structured inputs. CONCLUSIONS: CaMPNet demonstrates robust and interpretable multimodal ECG-based diagnosis, offering a scalable framework for comorbidity screening and continual learning under real-world temporal dynamics.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".