Cardiovascular Stress Detection: How to Make It Explainable and Privacy-Preserving
Bibliographic record
Abstract
We propose a hybrid approach, combining machine learning (ML) and machine reasoning (MR) framework for detecting an elevated cognitive and physical workload level, also called stress, given both the cardiovascular physiological biomarkers and contextual data such as lifestyle habits (smoking, alcohol, stimulant use, physical-versus-mental labor) and demographic attributes (age, sex). We aim to identify stress using a hybrid ML–MR method that balances accuracy, interpretability, and privacy. The contextual data were combined with the ML-classified cardiovascular stress responses using a probabilistic causal model such as the Bayesian Network (BN), which provides explainability through causal relationships. Next, we explore how this framework can be used for privacy-preserving by using BNs to generate synthetic data that retain the distributions of the real data while making the real subjects unidentifiable from their attributes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".