decision-curve-analysis
OfficialAssess AI model clinical utility with DCA.
AuthorEvidenceOS
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This Skill addresses the critical gap in evaluating health AI models by determining if they lead to better clinical decisions, beyond just discrimination or calibration.
Core Features & Use Cases
- Net Benefit Calculation: Quantifies the clinical utility of a model across various risk thresholds.
- Decision Curve Visualization: Plots net benefit against threshold probability to identify where a model adds value compared to standard strategies.
- Use Case: Evaluating a new AI model that predicts sepsis risk. DCA helps determine if using the model to guide treatment decisions (e.g., initiating antibiotics) is more beneficial than treating all patients or treating none, across different patient risk levels.
Quick Start
Run the decision-curve-analysis skill to generate a DCA plot for your model predictions and true outcomes.
Dependency Matrix
Required Modules
dcurvespandasnumpy
Components
scriptsreferences
💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: decision-curve-analysis Download link: https://github.com/EvidenceOS/awesome-health-ai-skills/archive/main.zip#decision-curve-analysis Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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