ClarusC64/clinical-five-node-mof-cascade-boundary-v0.4

What this repo does

This dataset models multi-organ failure cascade instability boundaries using a five-node physiological interaction system.

Clarus v0.4 datasets focus on detecting whether systems lie on the edge of cascade instability.

The objective is to determine when the multi-organ failure system is so close to collapse that even small perturbations trigger systemic failure.

Core cascade nodes

metabolicstress physiologicalbuffer interventiondelay organcoupling inflammatory_load

These nodes represent interacting components of multi-organ failure physiology.

metabolic_stress captures systemic metabolic burden and energetic instability.

physiological_buffer represents remaining resilience and compensatory reserve.

intervention_delay reflects delayed recognition, escalation, or corrective treatment.

organ_coupling represents propagation of dysfunction across organ systems.

inflammatory_load represents escalating inflammatory pressure contributing to organ failure spread.

Trajectory layer

drift_gradient

Range -1 to +1

Negative values indicate stabilization.

Positive values indicate drift toward cascade.

Dynamic forecasting layer

driftvelocity driftacceleration boundary_distance

These describe how quickly the system is approaching collapse.

Boundary discovery layer

Two variables capture proximity to instability.

perturbationradius collapsetrigger

These convert the dataset into an adversarial cascade boundary discovery benchmark.

Boundary variable definitions

perturbation_radius

Minimum perturbation needed to push the system into cascade.

Range 0 to 1.

Small values indicate extreme fragility.

collapse_trigger

Binary indicator showing whether the perturbation produced cascade.

0 stable 1 cascade

collapse_trigger is included as an observed perturbation response feature.

It is not the prediction target.

The prediction task is to identify the underlying boundary-risk state.

Prediction target

labelmofcascade

A positive label is triggered when either condition holds.

boundary_distance < 0.10

or

perturbation_radius < 0.08

These thresholds represent proximity to the instability manifold and minimal perturbation collapse risk.

Row structure

scenario_id

metabolicstress physiologicalbuffer interventiondelay organcoupling inflammatory_load

driftgradient driftvelocity driftacceleration boundarydistance

perturbationradius collapsetrigger

labelmofcascade

Files

data/train.csv labeled training examples

data/tester.csv unlabeled evaluation examples

scorer.py binary boundary detection evaluation script

README.md dataset documentation

Evaluation

The scorer reports

accuracy precision recallboundarydetection falsesaferate f1 confusion_matrix

Primary metric

recallboundarydetection

Secondary diagnostic metric

falsesaferate

Structural Note

Clarus dataset progression

v0.1 cascade detection v0.2 trajectory detection v0.3 dynamic forecasting v0.4 boundary discovery

Production Deployment

Research dataset for instability detection and cascade modeling.

Not intended for clinical decision use.

Enterprise & Research Collaboration

For dataset expansion, custom coherence scorers, or deployment architecture:

team@clarusinvariant.com

Instability is detectable. Governance determines whether it propagates.