A universal AutoScore framework to develop interpretable scoring systems for predicting common types of clinical
Feng Xie1, Yilin Ning2, Mingxuan Liu2
1Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore 169857, Singapore; Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore 169857, Singapore.
STAR Protocols
|May 13, 2023
Summary
The AutoScore package provides a protocol for creating data-driven clinical scoring systems for various outcomes. This framework ensures scores are understandable, explainable, and evidence-based for clinical applications.
Area of Science:
- Biostatistics
- Clinical Informatics
- Health Data Science
Background:
- Clinical scoring systems are crucial for decision-making.
- Developing accurate and explainable scores can be complex.
- Data-driven approaches offer potential for improved clinical tools.
Purpose of the Study:
- To present a protocol for developing clinical scoring systems using the AutoScore package.
- To guide researchers in creating data-driven scores for binary, survival, and ordinal outcomes.
- To facilitate the generation of understandable and explainable clinical scores.
Main Methods:
- Utilized the open-source AutoScore package for score development.
- Detailed steps for package installation, data processing, and variable ranking.
- Employed iterative processes for variable selection, score generation, fine-tuning, and evaluation.
Main Results:
- The protocol enables the creation of data-driven clinical scoring systems.
- Scores generated are designed to be understandable and explainable.
- The framework supports various outcome types including binary, survival, and ordinal.
Conclusions:
- The AutoScore framework offers a robust method for developing clinical scores.
- This protocol aids in integrating data-driven evidence with clinical knowledge.
- The open-source package promotes accessible and reproducible clinical score development.
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