ClarusC64/clinical-quad-perfusion-buffer-lag-coupling-trauma-deterioration-v0.1

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

perfusionpressure physiologicalbuffer interventiondelay organcoupling

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

Prediction target

labeltraumadeterioration

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:

scenarioid perfusionpressure physiologicalbuffer interventiondelay organcoupling labeltrauma_deterioration

Values are normalized between 0 and 1 for training simplicity.

Lower perfusionpressure and lower physiologicalbuffer increase risk.

Higher interventiondelay and higher organcoupling 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 recallcascadedetection falsesaferate f1 confusion matrix

The primary metric is recallcascadedetection because the main task is to detect approaching deterioration rather than simply optimize overall accuracy.

falsesaferate 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.