Explainable Artificial Intelligence–driven Risk Assessment for Malignant Ventricular Arrhythmia and Mortality in Acute Myocardial Infarction
Bibliographic record
Abstract
BACKGROUND: Malignant ventricular arrhythmia (MVA) is a severe complication that can occur after acute myocardial infarction, often leading to sudden cardiac death. METHODS: A total of 4471 patients from 2 medical centers were included in this study. The primary endpoint was a composite of MVA and in-hospital death. Seven state-of-the-art artificial intelligence (AI) models were developed and optimized by nested 5-fold cross-validation. Predictive performance was evaluated using the area under the receiver-operating characteristic (AUROC) curve, the calibration curve, and the decision analysis curve. RESULTS: Among the enrolled patients, 3456 were assessed for model development and validation and 1015 patients from another medical center were asssessed for external validation. In the validation group, the eXtreme Gradient Boosting (XGBoost) model achieved the highest AUROC of 0.792 (95% confidence interval [CI] 0.740-0.845) for the composite endpoint. The Light Gradient Boosting Machine (LightGBM) model demonstrated superior performance for MVA prediction (AUROC = 0.827, 95% CI 0.768-0.885), whereas the Random Forest (RF) model outperformed the others for mortality prediction (AUROC = 0.784, 95% CI 0.720-0.848). In the external validation group, the AUROC of the XGBoost model with 15 variables for predicting the primary endpoint event was 0.726. The AUROCs were 0.704 for the LightGBM model with 15 variables for predicting MVA and 0.823 for the RF model with 20 variables for predicting in-hospital death. The Web-based prediction system showed real-time risk assessment capabilities. CONCLUSIONS: Our study presents an interpretable AI framework integrating multimodel analysis for acute myocardial infarction risk management. The system offers clinicians a validated tool for personalized risk assessment that can potentially improve patient outcomes through early intervention strategies.
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 | 0.004 | 0.009 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".