hART: Deep learning-informed lifespan heart failure risk trajectories
Bibliographic record
Abstract
BACKGROUND: Heart failure (HF) results in persistent risk and long-term comorbidities. This is particularly true for patients with lifelong HF sequelae of cardiovascular disease such as patients with congenital heart disease (CHD). PURPOSE: We developed hART (heart failure Attentive Risk Trajectory), a deep-learning model to predict HF trajectories in CHD patients. METHODS: hART is designed to capture the contextual relationships between medical events within a patient's history. It is trained to predict future HF risk by using the masked self-attention mechanism that forces it to focus only on the most relevant segments of the past medical events. RESULTS: To demonstrate the utility of hART, we used a large cohort containing healthcare administrative data from the Quebec CHD database (137,493 patients, 35-year follow-up). hART achieves an area under the precision-recall of 28% for HF risk prediction, which is 33% improvement over existing methods. Patients with severe CHD lesion showed a consistently elevated predicted HF risks throughout their lifespan, and patients with genetic syndromes exhibited elevated HF risks until the age of 50. The impact of the birth condition decreases on long-term HF risk. The timing of interventions such as arrhythmia surgery had varying impacts on the lifespan HF risk among the individuals. Arrhythmic surgery performed at a younger age had minimal long-term effects on HF risk, while surgeries during adulthood had a significant lasting impact. CONCLUSION: Together, we show that hART can detect meaningful lifelong HF risk in CHD patients by capturing both long and short-range dependencies in their past medical events.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".