scenario_id int64 | oxygen_demand float64 | physiological_buffer float64 | intervention_delay float64 | organ_coupling float64 | gas_exchange_instability float64 | label_respiratory_cascade int64 |
|---|---|---|---|---|---|---|
1 | 0.85 | 0.27 | 0.67 | 0.72 | 0.76 | 1 |
2 | 0.24 | 0.86 | 0.1 | 0.21 | 0.18 | 0 |
3 | 0.73 | 0.4 | 0.59 | 0.64 | 0.68 | 1 |
4 | 0.31 | 0.79 | 0.16 | 0.27 | 0.24 | 0 |
5 | 0.81 | 0.33 | 0.63 | 0.69 | 0.72 | 1 |
6 | 0.2 | 0.88 | 0.09 | 0.18 | 0.16 | 0 |
7 | 0.7 | 0.45 | 0.56 | 0.61 | 0.65 | 1 |
8 | 0.27 | 0.82 | 0.13 | 0.24 | 0.21 | 0 |
9 | 0.77 | 0.36 | 0.6 | 0.66 | 0.7 | 1 |
10 | 0.22 | 0.85 | 0.1 | 0.2 | 0.17 | 0 |
What this repo does
This dataset models the transition from compensated respiratory stress to respiratory cascade using a five-variable interaction structure.
The goal is to detect when a patient is drifting toward respiratory boundary failure before overt systemic collapse is fully established.
Respiratory deterioration often unfolds as a cascade: oxygen demand rises, physiological reserve narrows, intervention delays reduce reversibility, organ interactions amplify instability, and gas-exchange instability reduces tissue-level recoverability.
The five-node structure extends the core Clarus clinical logic beyond a quad and captures a higher-order cascade surface.
Core five-node structure
oxygen_demand
physiological_buffer
intervention_delay
organ_coupling
gas_exchange_instability
oxygen_demand reflects the respiratory burden placed on the patient.
physiological_buffer reflects reserve capacity and resilience against respiratory deterioration.
intervention_delay captures lag in oxygen escalation, bronchodilation, non-invasive support, ventilation, imaging, or escalation.
organ_coupling reflects how respiratory dysfunction interacts with circulatory, renal, and metabolic instability.
gas_exchange_instability reflects failure of effective oxygenation and ventilation at the alveolar and tissue level.
Clinical Variable Mapping
| Node | Clinical Measurements | Typical Risk Signals |
|---|---|---|
| oxygen_demand | respiratory rate, oxygen requirement, work of breathing, PaO2/FiO2 ratio | rising oxygen requirement, RR > 30, falling P/F ratio |
| physiological_buffer | age, baseline lung reserve, albumin, frailty, chronic lung disease | low reserve, chronic lung disease, frailty |
| intervention_delay | delay in oxygen escalation, nebulizers, NIV, intubation, imaging | delayed respiratory support or delayed escalation |
| organ_coupling | lactate trend, hypotension, renal spillover, SOFA interaction | respiratory decline spilling into systemic instability |
| gas_exchange_instability | SpO2 variability, PaCO2 rise, A–a gradient, worsening compliance | unstable saturation, rising CO2, worsening gas exchange |
Prediction target
label_respiratory_cascade
Binary classification.
0 = respiratory state remains stable or recoverable
1 = respiratory cascade boundary is approaching
Binary simplification note
The Cascade Transition framework supports a full five-stage trajectory:
0 stable regime
1 deterioration drift
2 near cascade boundary
3 active cascade propagation
4 recovery trajectory
This v0.1 dataset intentionally uses a binary formulation.
Binary classification is easier to validate clinically and aligns with how respiratory monitoring and escalation systems are used in practice. Operational systems usually require a clear alert condition rather than a multi-stage taxonomy.
The full five-stage structure remains part of the broader framework and may appear in future dataset versions.
Why five nodes here
Most of the clinical suite uses quad structure because quad coupling is easier to validate, explain, and deploy.
This repo is a deliberate flagship extension.
The additional fifth node captures a clinically decisive layer that is often critical in respiratory progression: gas-exchange breakdown. That makes this dataset suitable as an advanced boundary set inside the wider clinical suite.
Row structure
Each row represents a simulated respiratory patient state.
Columns:
scenario_id
oxygen_demand
physiological_buffer
intervention_delay
organ_coupling
gas_exchange_instability
label_respiratory_cascade
Values are normalized between 0 and 1 for training simplicity.
Higher oxygen_demand increases risk.
Lower physiological_buffer increases risk.
Higher intervention_delay, higher organ_coupling, and higher gas_exchange_instability increase risk.
Files
data/train.csv
data/tester.csv
scorer.py
train.csv contains labeled rows.
tester.csv contains unlabeled rows with the same schema except for the target label.
scorer.py evaluates binary classification performance.
Evaluation
The scorer computes:
accuracy
precision
recall_cascade_detection
false_safe_rate
f1
confusion matrix
The primary metric is recall_cascade_detection because the main task is to detect approaching respiratory boundary failure rather than simply optimize overall accuracy.
false_safe_rate captures the proportion of positive danger cases missed by the model.
License
MIT
Structural Note
This dataset is part of the Clarus Cascade Transition Dataset family.
These datasets model how complex systems move from stable regimes into cascading failure states.
In respiratory medicine this corresponds to the transition from compensating oxygen stress into systemic respiratory deterioration.
This five-node dataset functions as an advanced boundary set within the clinical suite.
Production Deployment
Potential applications include:
respiratory boundary detection
ICU respiratory monitoring
advanced clinical deterioration modeling
decision support for unstable respiratory patients
research prototypes for higher-order cascade detection
Enterprise & Research Collaboration
Clarus datasets explore stability boundaries in complex systems including clinical deterioration, infrastructure failure, financial contagion, and AI system stability.
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