README.md

---
language:
- en
license: mit
pretty_name: Clarus Clinical Stability Benchmark
tags:
- clarusc64
- stability-reasoning
- clinical-benchmark
- tabular-reasoning
- system-stability
- trajectory-analysis
---
# Benchmark Documentation

Core

- benchmark_structure.md
- benchmark_matrix.md
- datasets.md

Evaluation

- evaluation_framework.md
- transfer_matrix.md
- clarus_score.md

Robustness

- missing_data_protocol.md
- imbalance_protocol.md
- robustness_suite.md

Theory

- stability_manifold.md
- stability_topology.md
- stability_mechanisms.md

Results

- baseline_results.md
- leaderboard.md

# Clarus Clinical Stability Benchmark

The Clarus Clinical Stability Benchmark evaluates whether machine learning models can detect **latent instability in complex clinical systems**.

Most tabular benchmarks reward models for learning correlations within a single dataset.  
The Clarus benchmark instead evaluates whether models can infer instability from **interacting proxy signals across multiple physiological and operational regimes**.

Each dataset represents a simplified regime in which instability emerges from multi-variable interaction rather than single-variable thresholds.

---

# Benchmark Concept

In real clinical systems, deterioration rarely occurs because one measurement crosses a threshold.

Instead, instability emerges when several components drift simultaneously.

Examples include:

- circulatory compensation failure  
- microvascular perfusion loss  
- metabolic energy collapse  
- respiratory control failure  
- endocrine dysregulation  
- thermoregulatory breakdown  
- coagulation instability  
- hospital operational overload  

Each dataset exposes a different regime while keeping the underlying structure similar:  
**instability arises from interacting system signals.**

The generative rules that determine the labels are intentionally not published.

Models must infer instability from observable proxies.

---

# Included Datasets

| Stability Regime | Dataset |
|---|---|
| Hemodynamic collapse | ClarusC64/clinical-hemodynamic-collapse-v0.1 |
| Sepsis trajectory instability | ClarusC64/clinical-sepsis-trajectory-instability-v0.1 |
| Intervention delay failure | ClarusC64/clinical-intervention-delay-failure-v0.1 |
| Organ coupling cascade | ClarusC64/clinical-organ-coupling-cascade-v0.1 |
| Recovery window detection | ClarusC64/clinical-recovery-window-detection-v0.1 |
| Ventilation–Perfusion instability | ClarusC64/clinical-ventilation-perfusion-instability-v0.1 |
| Hemorrhage compensation collapse | ClarusC64/clinical-hemorrhage-compensation-collapse-v0.1 |
| Electrolyte instability | ClarusC64/clinical-electrolyte-instability-v0.1 |
| Microcirculation instability | ClarusC64/clinical-microcirculation-instability-v0.1 |
| Endocrine instability | ClarusC64/clinical-endocrine-instability-v0.1 |
| Thermoregulation instability | ClarusC64/clinical-thermoregulation-instability-v0.1 |
| Cellular energy instability | ClarusC64/clinical-cellular-energy-instability-v0.1 |
| Respiratory drive instability | ClarusC64/clinical-respiratory-drive-instability-v0.1 |
| Coagulation instability | ClarusC64/clinical-coagulation-instability-v0.1 |
| Hospital operational collapse | ClarusC64/clinical-hospital-operational-collapse-v0.1 |

Each dataset repository contains:
data/train.csv
data/test.csv
scorer.py
README.md


---

# Evaluation Protocol

Predictions must follow the format:


scenario_id,prediction


Example:


MC101,0
MC102,1


Evaluation is performed using the **scorer located in the dataset repository**.

Example:


python scorer.py --predictions predictions.csv --truth data/test.csv --output metrics.json


The `--truth` path refers to the dataset's local `data/test.csv` file.

Metrics reported include:

- accuracy
- precision
- recall
- f1
- confusion matrix

---

# Benchmark Tasks

The benchmark supports three evaluation settings.

## 1 Single-Dataset Evaluation

Train and test on the same dataset.

Purpose:

Measure baseline performance within a single stability regime.

---

## 2 Cross-Regime Transfer

Train on one regime and test on another.

Example:

Train → clinical-hemodynamic-collapse-v0.1  
Test → clinical-microcirculation-instability-v0.1

Purpose:

Determine whether models learn **general stability reasoning** rather than dataset-specific correlations.

---

## 3 Multi-Regime Training

Train on multiple datasets simultaneously.

Evaluate performance across all regimes.

Purpose:

Test whether models can learn shared stability representations across physiological systems.

---

# Dataset Design Principles

The Clarus datasets follow several explicit design rules.

### No Single-Feature Dominance

No observable variable strongly predicts the label independently.

Target:

|correlation| < 0.30

---

### Interaction-Based Labels

Instability emerges from interactions between multiple variables rather than isolated thresholds.

---

### Adversarial Symmetry

Rows with nearly identical values may produce opposite labels.

This prevents trivial heuristics.

---

### Decoy Variables

Some variables appear meaningful but do not determine the label independently.

---

### Hidden Generative Logic

The dataset generator and rule equations are intentionally not published.

Models must infer instability from proxy signals.

---

# Baseline Results

Reference baseline experiments are provided in:

baseline_results.md


These establish approximate difficulty levels for common tabular models.

---

# Benchmark Architecture

The benchmark can be interpreted as observing a **shared stability manifold** through different clinical regimes.

Each dataset exposes a different control system while preserving the underlying concept of instability emerging from interacting signals.

Additional details are provided in:


stability_manifold.md


---

# Research Applications

The benchmark supports research into:

- system stability reasoning
- interaction-based tabular learning
- cross-domain generalization
- clinical early warning modeling
- infrastructure and system risk detection

---

Quick Start
# Quick Start

This example demonstrates how to evaluate a simple model on one Clarus dataset.

---

## 1 Install dependencies

Example environment:


pip install pandas scikit-learn


---

## 2 Load the dataset


train = data/train.csv
test = data/test.csv


---

## 3 Train a simple baseline model

Example using logistic regression:


import pandas as pd
from sklearn.linear_model import LogisticRegression

train = pd.read_csv("data/train.csv")

X = train.drop(columns=["scenario_id","label"])
y = train["label"]

model = LogisticRegression()
model.fit(X, y)


---

## 4 Generate predictions


test = pd.read_csv("data/test.csv")

X_test = test.drop(columns=["scenario_id","label"])

pred = model.predict(X_test)

out = pd.DataFrame({
"scenario_id": test["scenario_id"],
"prediction": pred
})

out.to_csv("predictions.csv", index=False)


---

## 5 Evaluate predictions

Run the official scorer:


python scorer.py --predictions predictions.csv --truth data/test.csv


The scorer returns:

- accuracy
- precision
- recall
- f1
- confusion matrix

# License

MIT