Abstract 4353134: Explainable Deep Learning Predicts Future Adverse Outcomes in Non-Ischemic Cardiomyopathy From Multi-domain Digital Health Data
Bibliographic record
Abstract
Background: Despite recognized phenotypic heterogeneity of idiopathic non-ischemic cardiomyopathy (NICM), management remains generalized and dominantly guided by left ventricular (LV) ejection fraction (EF) and New York Heart Association class. Deep learning (DL) models can integrate complex, multimodal phenomics data to support individualized prognostication; however, are considered black box models that limit clinical adoption. We developed an explainable DL model, DeepPhenome-NICM, for patient-specific prediction of major adverse cardiac events (MACE) in NICM leveraging multi-domain phenomics data captured at time of cardiovascular MR (CMR) imaging. Methods: 1,142 patients with CMR confirmed diagnosis of NICM were identified from the Cardiovascular Imaging Registry of Calgary, defined as LV EF <50% in the absence of any identifiable ischaemic or non-ischaemic aetiology. All patients underwent baseline health questionnaires with standardized reporting at the time of CMR imaging and were followed for a minimum of 6 months for the composite outcome of all-cause mortality, survived cardiac arrest, ventricular tachycardia, or heart failure hospitalization. A total of 50 routinely captured variables were included in a final trained DL survival model (DeepPhenome-NICM), inclusive of patient-reported, CMR-derived, and electronic health record-derived variables. Data were split into training (80%) and test (20%) sets. Model performance was assessed on the test set. Shapley values, a measure of additive feature contribution to model prediction, were estimated to deliver model explainability. Results: Baseline characteristics of the study population are reported in Table 1. Over a median follow-up of 3.8 years, 210 patients (18.4%) experienced MACE. Using the hold-out test set, the DeepPhenome-NICM model achieved a mean time-dependent AUC of 0.83 (95% CI 0.75-0.89) with a 1- and 5-year AUC of 0.87 (0.79-0.93) and 0.82 (0.73-0.89), respectively. Stratification of patients by the median predicted patient-specific risk score yielded significant discrimination of event-free survival, with the high-risk group experiencing a 4.3-fold increased risk (HR; 95% CI 2.1-8.9; p<0.001; Figure 1) . Figure 2 shows the respective influence of top predictors on model prediction. Conclusions: DeepPhenome-NICM is a multimodal DL model that identifies high risk patients with NICM at time of CMR using a composite phenomics based approach. External validation of this model is planned.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".