---
language: en
license: mit
task_categories:
- text-classification
tags:
- clinical-trials
- cascade-transition
- five-node-cascade
size_categories:
- 1K<n<10K
pretty_name: Clinical Five Node Respiratory Cascade Boundary
---

# 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.