ClarusC64/clinical-compensation-collapse-sepsis-v1

Clinical Compensation Collapse Sepsis Detection Overview

This dataset tests whether a model can detect when a clinical system is moving from physiological compensation toward collapse.

In many critical illnesses such as sepsis, patients can appear stable for a period of time because the body temporarily compensates for rising stress. During this phase the system absorbs disturbances through physiological reserve.

However, compensation cannot continue indefinitely. Once reserve capacity is exhausted, the system rapidly transitions toward decompensation and collapse.

The benchmark evaluates whether models can detect the early structural signals of compensation failure before visible collapse occurs.

The compensation problem

Compensation allows complex biological systems to maintain stability under stress.

During compensation:

physiological buffers absorb disturbances vital signs may remain temporarily stable the system appears stable at the surface level

As stress increases, buffer capacity declines. Eventually the system approaches a boundary where compensation becomes unsustainable.

When this boundary is crossed, the system rapidly enters decompensation, where physiological failure accelerates.

Detecting this transition early is critical for effective intervention.

Core system geometry

Each row represents a simplified clinical dynamical system described using structural variables.

pressure Overall physiological stress acting on the system.

buffer_capacity Remaining physiological reserve available to absorb stress.

intervention_lag Delay between deterioration and corrective treatment.

coupling_strength Degree of interaction between physiological subsystems.

trajectory_drift Directional movement of the system state toward stability or collapse.

Together these variables describe the geometry of compensation and decompensation.

Clinical geometry mapping

The structural variables correspond to simplified abstractions of real clinical measurements.

Structural Variable Clinical Interpretation Possible Real Signals pressure Overall physiological stress heart rate, mean arterial pressure, lactate buffer_capacity Remaining physiological reserve organ function markers, oxygen reserve intervention_lag Delay before treatment time to antibiotics, time to fluids coupling_strength Interaction between organ systems inflammatory signaling, organ cross-talk trajectory_drift Direction of system movement trend in lactate, organ function trajectory

These variables allow the dataset to represent system-level instability rather than isolated clinical signals.

Prediction target

label_transition

0 = system still compensated 1 = system approaching or entering decompensation

The task is to determine whether the system is still operating within its compensatory capacity or is moving toward collapse.

Row structure

Each row represents a synthetic clinical scenario.

Columns:

scenario_id pressure buffer_capacity intervention_lag coupling_strength trajectory_drift

Training rows include the label. Tester rows omit the label.

Evaluation

The scoring script reports:

accuracy precision recall f1 specificity negative predictive value (npv)

Primary metric recall

Secondary metric f1

Recall is prioritized because detecting early collapse risk is more important than avoiding false alarms.

Why this benchmark matters

Clinical deterioration rarely occurs suddenly. Instead, systems often move gradually toward collapse while appearing temporarily stable.

Detecting the failure of physiological compensation allows clinicians to intervene before irreversible damage occurs.

The benchmark therefore tests whether models can detect early instability in dynamical systems, rather than simply recognizing late-stage failure.

Structural note

This dataset exposes system geometry while keeping the generator used to construct the scenarios private.

The benchmark is designed to test whether models can identify structural transitions rather than memorizing statistical patterns.

Clarus Stability Geometry Benchmarks

This dataset is part of a broader family of instability geometry probes.

Related datasets include:

clinical-fork-point-sepsis-transition-v1 clinical-organ-failure-cascade-v1 clinical-recovery-window-sepsis-v1 clinical-intervention-alignment-sepsis-v1 clinical-recovery-stability-sepsis-v1 clinical-false-stability-sepsis-v1

Together these benchmarks map the lifecycle of instability and recovery in complex clinical systems.

License

MIT