scenario_id int64 | perfusion_pressure float64 | physiological_buffer float64 | intervention_delay float64 | organ_coupling float64 | label_trauma_deterioration int64 |
|---|---|---|---|---|---|
1 | 0.28 | 0.36 | 0.66 | 0.71 | 1 |
2 | 0.82 | 0.79 | 0.11 | 0.21 | 0 |
3 | 0.41 | 0.48 | 0.58 | 0.63 | 1 |
4 | 0.76 | 0.72 | 0.16 | 0.27 | 0 |
5 | 0.33 | 0.39 | 0.61 | 0.68 | 1 |
6 | 0.88 | 0.83 | 0.09 | 0.18 | 0 |
7 | 0.45 | 0.51 | 0.56 | 0.6 | 1 |
8 | 0.8 | 0.75 | 0.14 | 0.24 | 0 |
9 | 0.37 | 0.42 | 0.59 | 0.66 | 1 |
10 | 0.84 | 0.81 | 0.12 | 0.22 | 0 |
What this repo does
This dataset models the transition from compensated trauma physiology to systemic deterioration using a four-variable coupling structure.
The goal is to detect when trauma patients are drifting toward hemodynamic collapse before overt shock or organ failure occurs.
Trauma deterioration often unfolds as a cascade: perfusion declines, physiological reserves are consumed, treatment delays amplify instability, and organ systems begin to couple into systemic failure.
The quad structure captures the key drivers of that transition.
Core quad
perfusion_pressure
physiological_buffer
intervention_delay
organ_coupling
perfusion_pressure reflects circulatory stability and tissue perfusion.
physiological_buffer reflects the patient’s reserve capacity to tolerate blood loss and acute stress.
intervention_delay captures lag in hemorrhage control, transfusion, imaging, or surgical intervention.
organ_coupling reflects how dysfunction in one system begins amplifying failure in others.
Clinical Variable Mapping
| Quad Variable | Clinical Measurements | Typical Risk Signals |
|---|---|---|
| perfusion_pressure | MAP, systolic blood pressure, shock index | MAP < 65, SBP < 90, shock index rising |
| physiological_buffer | hemoglobin, age, comorbidity burden, baseline reserve | Hb < 8, frailty, limited reserve |
| intervention_delay | time to hemorrhage control, transfusion delay, surgical delay | delayed transfusion or source control |
| organ_coupling | lactate trend, base deficit, SOFA progression | lactate rising, worsening organ interaction |
Prediction target
label_trauma_deterioration
Binary classification.
0 = trauma physiology remains stable or recoverable
1 = trauma cascade deterioration 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 trauma monitoring and escalation systems are deployed 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.
Row structure
Each row represents a simulated trauma patient state.
Columns:
scenario_id
perfusion_pressure
physiological_buffer
intervention_delay
organ_coupling
label_trauma_deterioration
Values are normalized between 0 and 1 for training simplicity.
Lower perfusion_pressure and lower physiological_buffer increase risk.
Higher intervention_delay and higher organ_coupling 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 deterioration 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 trauma care this corresponds to the transition from compensated physiology into hemorrhagic or circulatory cascade.
Production Deployment
Potential applications include:
trauma deterioration early warning
emergency department triage support
hemorrhage escalation monitoring
critical care trauma decision support
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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