AutoScore
An Interpretable Machine Learning-Based Automatic Clinical Score Generator
A novel interpretable machine learning-based framework to automate the development of a clinical scoring model for predefined outcomes. Our novel framework consists of six modules: variable ranking with machine learning, variable transformation, score derivation, model selection, domain knowledge-based score fine-tuning, and performance evaluation.The The original AutoScore structure is described in the research paperdoi:10.2196/21798. A full tutorial can be found herehttps://nliulab.github.io/AutoScore/. Users or clinicians could seamlessly generate parsimonious sparse-score risk models (i.e., risk scores), which can be easily implemented and validated in clinical practice. We hope to see its application in various medical case studies.
- Version1.0.0
- R version≥ 3.5.0
- LicenseGPL-2
- LicenseGPL-3
- Needs compilation?No
- AutoScore citation info
- Last release10/15/2022
Documentation
Team
Feng Xie
Yilin Ning
Han Yuan
Mingxuan Liu
Seyed Ehsan Saffari
Siqi Li
Bibhas Chakraborty
Nan Liu
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- Imports18 packages
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