--- 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