ClarusC64/clinical-quad-surgical-stress-buffer-lag-coupling-postop-collapse-v0.2

What this repo does

This dataset evaluates whether machine learning models can detect postoperative physiological collapse using both system state and system trajectory.

Earlier Clarus datasets in the v0.1 series tested whether models could classify system state alone.

Clarus v0.2 datasets introduce a trajectory signal so models must determine not only where the system is but where it is moving inside state space.

The benchmark therefore tests whether models can read trajectory inside state space and detect approaching instability even when the current state appears locally stable.

Core quad

surgicalstress physiologicalbuffer interventiondelay organcoupling

These variables represent interacting physiological pressures influencing postoperative stability.

All variables are normalized between 0 and 1.

Together they define the current position of the system inside the physiological stability manifold.

Trajectory Layer

This dataset includes a trajectory variable called drift_gradient.

drift_gradient measures the directional alignment between the system motion vector and the instability gradient of the system potential field.

Positive values indicate motion toward cascade. Negative values indicate motion toward recovery.

This converts the dataset from a static cascade classifier into a trajectory-aware cascade detection benchmark.

The key test is whether models can read trajectory inside state space rather than relying only on static variable levels.

Drift definition

Let system state be represented by vector x(t)

Previous state

x(t−1)

System motion

Δx = x(t) − x(t−1)

Instability gradient

g = ∇Φ(x)

Drift alignment

drift_gradient = (Δx · g) / (||Δx|| ||g||)

Interpretation

+1 system moving directly toward collapse 0 neutral movement −1 system moving toward recovery

Operational meaning

A positive drift_gradient such as +0.65 means the postoperative physiological trajectory is strongly aligned toward collapse even if the current clinical state appears moderately stable.

This is the false stability detection problem encoded by v0.2 datasets.

Prediction target

labelpostopcollapse

The label identifies whether the system is undergoing a postoperative physiological collapse cascade.

High drift toward cascade can trigger a positive label even when state variables remain within nominal ranges.

Binary simplification note

Postoperative deterioration is continuous in real clinical systems.

For benchmarking purposes the outcome is simplified to binary classification:

0 stable postoperative regime 1 postoperative collapse cascade

Row structure

scenarioid surgicalstress physiologicalbuffer interventiondelay organcoupling driftgradient labelpostopcollapse

State variables range between 0 and 1.

drift_gradient ranges between −1 and +1.

Files

data/train.csv data/tester.csv scorer.py README.md

Evaluation

Binary classification metrics

accuracy precision recallcascadedetection falsesaferate f1 confusion_matrix

Primary metric

recallcascadedetection

This prioritizes early cascade detection before visible collapse.

License

MIT

Structural Note

Clarus datasets explore instability dynamics in coupled systems.

The v0.2 series introduces trajectory awareness to evaluate whether models can detect movement toward failure boundaries rather than relying only on static state classification.

Production Deployment

These datasets support experimentation and benchmarking across domains including:

clinical deterioration infrastructure instability financial contagion AI multi-agent systems supply chain collapse

Enterprise & Research Collaboration

Organizations interested in trajectory-aware cascade detection or operational early-warning systems may collaborate with the Clarus project.