Datasets:

ClarusC64
/

clinical-quad-cardiac-load-reserve-lag-coupling-heart-failure-transition-v0.5

scenario_id int64	cardiac_load float64	cardiac_reserve float64	intervention_delay float64	organ_coupling float64	drift_gradient float64	drift_velocity float64	drift_acceleration float64	boundary_distance float64	perturbation_radius float64	collapse_trigger int64	recovery_distance float64	recovery_gradient float64	return_feasibility int64	label_heart_failure_transition int64
1	0.82	0.25	0.64	0.71	0.69	0.6	0.34	0.04	0.02	1	0.76	0.48	0	1
2	0.74	0.33	0.55	0.67	0.51	0.46	0.2	0.08	0.05	0	0.61	0.18	1	0
3	0.66	0.38	0.49	0.59	0.38	0.36	0.15	0.13	0.07	0	0.44	-0.12	1	0
4	0.79	0.27	0.61	0.69	0.63	0.55	0.28	0.05	0.03	1	0.7	0.36	0	1
5	0.58	0.45	0.44	0.52	0.14	0.26	0.03	0.2	0.1	0	0.23	-0.39	1	0
6	0.71	0.35	0.52	0.64	0.47	0.42	0.19	0.1	0.06	0	0.49	0.04	1	0
7	0.88	0.19	0.72	0.78	0.77	0.67	0.41	0.02	0.01	1	0.83	0.55	0	1
8	0.63	0.42	0.46	0.55	0.22	0.3	0.07	0.17	0.09	0	0.32	-0.25	1	0
9	0.84	0.23	0.67	0.73	0.72	0.63	0.35	0.03	0.02	1	0.79	0.46	0	1

What this repo does

This repository provides a Clarus v0.5 cascade recovery geometry dataset modeling heart failure transition.

The dataset evaluates whether a cardiac system experiencing rising load remains recoverable or has crossed an irreversible deterioration boundary.

The dataset combines:

• a cardiac instability quad
• trajectory dynamics
• boundary discovery signals
• recovery geometry variables

Models must determine whether cardiac deterioration remains reversible.

Core quad

cardiac_load
cardiac_reserve
intervention_delay
organ_coupling

Interpretation

cardiac_load
Represents hemodynamic load placed on the heart.

cardiac_reserve
Represents myocardial and systemic capacity to compensate for load.

intervention_delay
Represents time lag before therapeutic intervention.

organ_coupling
Represents propagation of cardiac stress into systemic organ networks.

Trajectory layer

drift_gradient

Range −1 to +1

Positive values indicate motion toward deterioration.

Negative values indicate motion toward recovery.

Dynamic forecasting layer

drift_velocity
drift_acceleration
boundary_distance

These variables describe the speed and direction of system movement within the stability landscape.

Boundary discovery layer

perturbation_radius
collapse_trigger

These variables represent fragility of the local stability basin.

Recovery geometry layer

recovery_distance
recovery_gradient
return_feasibility

These variables determine whether recovery remains structurally possible.

Prediction target

label_heart_failure_transition

Collapse threshold

collapse_threshold = 0.05

Label logic

Positive labels trigger when:

boundary_distance < 0.05

return_feasibility = 0

Files

data/train.csv
data/tester.csv
scorer.py
cli.py
README.md

Evaluation

Metrics

accuracy
precision
recall_irreversible_detection
false_recovery_rate
f1
confusion_matrix

Primary metric

recall_irreversible_detection

Secondary metric

false_recovery_rate

License

MIT

Structural Note

Clarus v0.5 introduces recovery geometry.

The framework distinguishes between unstable cardiac states that remain recoverable and those that have crossed into irreversible heart failure transition.

Production Deployment

Recovery geometry datasets help model cardiac deterioration timing and intervention feasibility.

Enterprise & Research Collaboration

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

team@clarusinvariant.com

Instability is detectable.
Governance determines whether it propagates.

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