ClarusC64/clinical-quad-pressure-buffer-lag-coupling-hemodynamic-shock-v0.1

What this repo does

This dataset models the transition from pressured but recoverable circulation to hemodynamic shock using a four-variable coupling structure.

The goal is to detect when a patient is drifting toward circulatory collapse before overt shock is fully established.

Hemodynamic shock often unfolds as a cascade: pressure stability degrades, physiological reserve narrows, treatment delays reduce reversibility, and organ systems begin to amplify one another’s failure.

The quad structure captures the core drivers of that transition.

Core quad

perfusionpressure physiologicalbuffer interventiondelay organcoupling

perfusion_pressure reflects circulatory adequacy and tissue perfusion.

physiological_buffer reflects the patient’s reserve capacity to tolerate hypotension and systemic stress.

intervention_delay captures lag in fluids, vasopressors, transfusion, source control, or escalation.

organ_coupling reflects how dysfunction in one organ system begins driving instability in others.

Clinical Variable Mapping

Prediction target

labelhemodynamicshock

Binary classification.

0 = circulation remains stable or recoverable 1 = hemodynamic shock transition 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 shock 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 patient state.

Columns:

scenarioid perfusionpressure physiologicalbuffer interventiondelay organcoupling labelhemodynamic_shock

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 shock 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 shock physiology this corresponds to the transition from pressured but compensating circulation into systemic perfusion collapse.

Production Deployment

Potential applications include:

shock early warning emergency department triage support ICU deterioration monitoring resuscitation escalation support critical care decision systems

Enterprise & Research Collaboration

Clarus datasets explore stability boundaries in complex systems including clinical deterioration, infrastructure failure, financial contagion, and AI system stability.