Clarus Clinical Stability Benchmark Matrix
The Clarus Clinical Stability Benchmark evaluates whether machine learning models can detect latent stability dynamics rather than relying on simple feature correlations.
Each dataset represents a specific clinical system domain and instability mechanism.
The benchmark also includes robustness variants that test whether models remain reliable under incomplete observations and class imbalance.
Benchmark Matrix
| Dataset | Domain | Instability Mechanism | Missing Data Variant | Class Imbalance Variant |
|---|---|---|---|---|
| clinical-hemodynamic-collapse | circulation | pressure collapse | ✓ | ✓ |
| clinical-perfusion-instability | circulation | microvascular perfusion failure | ✓ | ✓ |
| clinical-oxygen-transport-instability | circulation | oxygen delivery failure | ✓ | ✓ |
| clinical-respiratory-drive-instability | respiration | ventilatory control failure | ✓ | ✓ |
| clinical-acid-base-instability | physiology | buffering collapse | ✓ | ✓ |
| clinical-renal-filtration-instability | renal | filtration failure | ✓ | ✓ |
| clinical-fluid-balance-instability | renal | volume dysregulation | ✓ | ✓ |
| clinical-electrolyte-instability | renal/metabolic | electrolyte imbalance | ✓ | ✓ |
| clinical-glucose-regulation-instability | metabolic | glucose feedback instability | ✓ | ✓ |
| clinical-nutritional-metabolic-instability | metabolic | metabolic supply failure | ✓ | ✓ |
| clinical-hormonal-feedback-instability | endocrine | endocrine feedback instability | ✓ | ✓ |
| clinical-drug-toxicity-instability | pharmacology | toxic accumulation | ✓ | ✓ |
| clinical-hemostasis-instability | hematology | coagulation imbalance | ✓ | ✓ |
| clinical-microvascular-instability | microcirculation | capillary flow heterogeneity | ✓ | ✓ |
| clinical-neurologic-deterioration-instability | neurology | intracranial perfusion instability | ✓ | ✓ |
Robustness Evaluation
The Clarus benchmark includes robustness variants designed to test whether models truly learn stability dynamics.
Missing Data Variants
Clinical observations are often incomplete.
To simulate this, trajectory datasets may include variants where observations are missing.
Supported variants:
- missing t0 — initial observation removed
- missing t1 — intermediate observation removed
- missing t2 — final observation removed
- random missing — one or more values randomly removed
These datasets evaluate whether models can infer stability dynamics from partial trajectories.
Class Imbalance Variants
Many real-world systems exhibit rare instability events.
To test robustness to prevalence shifts, datasets may include variants with altered class distributions.
Supported regimes:
- balanced (50 / 50)
- mild imbalance (70 / 30)
- severe imbalance (90 / 10)
- extreme imbalance (99 / 1)
These variants test whether models rely on true stability reasoning rather than prevalence heuristics.
Benchmark Objective
A robust stability model should remain reliable across:
- multiple instability mechanisms
- different clinical domains
- incomplete observations
- rare-event prevalence conditions
Models that rely on shallow correlations or class frequency will degrade under these evaluation regimes.
Structural Note
Each dataset reflects latent stability geometry expressed through observable clinical proxies.
The dataset generator and latent stability rules are not included in the benchmark repositories.