scenario_id int64 | oxygen_demand float64 | pulmonary_buffer float64 | intervention_delay float64 | organ_coupling float64 | ventilation_stability float64 | drift_gradient float64 | drift_velocity float64 | drift_acceleration float64 | boundary_distance float64 | label_respiratory_cascade_boundary int64 |
|---|---|---|---|---|---|---|---|---|---|---|
1 | 0.44 | 0.74 | 0.2 | 0.26 | 0.76 | 0.57 | 0.13 | 0.01 | 0.13 | 0 |
2 | 0.53 | 0.67 | 0.3 | 0.34 | 0.62 | 0.72 | 0.23 | 0.08 | 0.07 | 1 |
3 | 0.41 | 0.79 | 0.18 | 0.23 | 0.81 | 0.47 | 0.1 | -0.02 | 0.18 | 0 |
4 | 0.58 | 0.6 | 0.36 | 0.41 | 0.55 | 0.83 | 0.29 | 0.11 | 0.04 | 1 |
5 | 0.45 | 0.73 | 0.21 | 0.27 | 0.72 | 0.61 | 0.16 | 0.03 | 0.1 | 0 |
6 | 0.61 | 0.57 | 0.39 | 0.44 | 0.49 | 0.88 | 0.32 | 0.14 | 0.03 | 1 |
7 | 0.47 | 0.71 | 0.22 | 0.28 | 0.7 | 0.63 | 0.18 | 0.04 | 0.09 | 0 |
8 | 0.55 | 0.63 | 0.33 | 0.38 | 0.58 | 0.77 | 0.26 | 0.09 | 0.05 | 1 |
9 | 0.43 | 0.76 | 0.19 | 0.24 | 0.78 | 0.52 | 0.12 | 0 | 0.15 | 0 |
10 | 0.63 | 0.55 | 0.41 | 0.46 | 0.46 | 0.91 | 0.34 | 0.16 | 0.02 | 1 |
What this repo does
This dataset models respiratory cascade boundary approach using a Clarus five-node coupling framework combined with trajectory and system dynamics.
The goal is to predict whether a patient is approaching the respiratory cascade boundary.
The dataset introduces a dynamic forecasting layer that allows models to reason about motion through the stability manifold rather than relying only on static physiological snapshots.
Core five-node cascade
oxygen_demand
pulmonary_buffer
intervention_delay
organ_coupling
ventilation_stability
These five variables represent the interacting physiological state controlling respiratory stability.
oxygen_demand
Represents respiratory burden such as rising oxygen requirement, work of breathing, gas exchange strain, or escalating ventilatory demand.
pulmonary_buffer
Represents patient reserve including pulmonary resilience, metabolic compensation, and tolerance to rising respiratory stress.
intervention_delay
Represents the delay between deterioration onset and effective clinical response.
organ_coupling
Represents cross-system interaction such as respiratory-cardiac coupling, inflammatory spillover, or multi-organ linkage.
ventilation_stability
Represents the remaining stability of gas exchange, ventilatory mechanics, and pulmonary reserve under escalating respiratory stress.
The five-node structure models how these variables interact to produce either recoverable dynamics or cascade deterioration.
Trajectory layer
drift_gradient represents the direction of motion in the system state space.
Values near +1 indicate motion toward instability.
Values near −1 indicate motion toward recovery.
This variable captures trajectory alignment with the instability boundary.
Dynamic forecasting layer
Three additional variables describe how the system moves through the stability manifold.
drift_velocity — speed of motion through state space
drift_acceleration — change in velocity across consecutive time steps
boundary_distance — proximity to the instability boundary
Together these variables allow models to estimate how rapidly instability is approaching rather than simply identifying its direction.
This converts the dataset from trajectory detection into dynamic cascade forecasting.
Dynamic variable definitions
drift_velocity
Magnitude of state change between consecutive time steps.
Definition
drift_velocity(t) = ||x(t) − x(t−1)||
Interpretation
Higher values indicate faster movement through the stability manifold.
Lower values indicate slower system evolution.
drift_acceleration
Rate of change of drift velocity across three consecutive snapshots.
Definition
drift_acceleration(t) = drift_velocity(t) − drift_velocity(t−1)
where
drift_velocity(t) = ||x(t) − x(t−1)||
Interpretation
Positive values indicate accelerating movement toward instability.
Negative values indicate deceleration or stabilization.
boundary_distance
Weighted metric distance between the current system state and the instability boundary.
Definition
Computed as weighted Euclidean distance from the current state vector to the nearest point on the instability boundary, normalized to the range 0 to 1.
Interpretation
0 indicates the system has reached the cascade boundary.
Lower values indicate minimal remaining stability margin.
Higher values indicate greater separation from collapse.
Prediction target
label_respiratory_cascade_boundary
Binary classification.
1 indicates the system is entering the respiratory cascade boundary regime.
0 indicates the system remains recoverable.
Binary simplification note
Real respiratory deterioration unfolds as a continuous physiological process.
This dataset encodes boundary approach as a binary classification problem to simplify model evaluation and benchmarking.
False stability example
The central challenge in this dataset is detecting cases that appear stable when viewed only through the five-node state variables.
Example
oxygen_demand 0.44
pulmonary_buffer 0.72
intervention_delay 0.21
organ_coupling 0.27
ventilation_stability 0.70
drift_gradient +0.66
drift_velocity 0.18
drift_acceleration +0.08
boundary_distance 0.07
label_respiratory_cascade_boundary 1
This row appears relatively safe if only the static state variables are considered.
However:
drift_gradient shows motion toward deterioration
drift_velocity shows active movement through state space
drift_acceleration shows increasing momentum
boundary_distance shows very little remaining stability margin
This is a false stability case.
The patient appears stable in a static snapshot but is dynamically close to deterioration.
Row structure
scenario_id
oxygen_demand
pulmonary_buffer
intervention_delay
organ_coupling
ventilation_stability
drift_gradient
drift_velocity
drift_acceleration
boundary_distance
label_respiratory_cascade_boundary
Files
data/train.csv
data/tester.csv
scorer.py
readme.md
Evaluation
Models are evaluated using binary classification metrics.
accuracy
precision
recall_cascade_detection
false_safe_rate
f1
confusion_matrix
Primary metric
recall_cascade_detection
Secondary diagnostic metric
false_safe_rate
The primary goal is detecting cascade onset rather than maximizing overall accuracy.
License
MIT
Structural Note
Clarus datasets encode cascade instability through interacting system variables rather than isolated predictors.
Collapse emerges from coupled system dynamics rather than from any single measurement crossing a threshold.
Production Deployment
These datasets support early warning models designed to detect deterioration trajectories before irreversible cascade occurs.
Such models may assist clinical monitoring systems by identifying dynamic instability patterns earlier than threshold-based alerts.
Enterprise & Research Collaboration
The Clarus dataset framework can be applied across multiple domains including clinical medicine, infrastructure monitoring, complex AI systems, and other environments where cascade instability must be detected before boundary crossing.
For dataset expansion, custom coherence scorers, or deployment architecture:
team@clarusinvariant.com
Instability is detectable.
Governance determines whether it propagates.
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