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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 paper<doi:10.2196/21798>. A full tutorial can be found here<https://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.
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Package details |
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| Author | Feng Xie [aut, cre] (<https://orcid.org/0000-0002-0215-667X>), Yilin Ning [aut] (<https://orcid.org/0000-0002-6758-4472>), Han Yuan [aut] (<https://orcid.org/0000-0002-2674-6068>), Mingxuan Liu [aut] (<https://orcid.org/0000-0002-4274-9613>), Seyed Ehsan Saffari [aut] (<https://orcid.org/0000-0002-6473-4375>), Siqi Li [aut] (<https://orcid.org/0000-0002-1660-105X>), Bibhas Chakraborty [aut] (<https://orcid.org/0000-0002-7366-0478>), Nan Liu [aut] (<https://orcid.org/0000-0003-3610-4883>) |
| Maintainer | Feng Xie <xief@u.duke.nus.edu> |
| License | GPL (>= 2) |
| Version | 1.0.0 |
| URL | https://github.com/nliulab/AutoScore |
| Package repository | View on CRAN |
| Installation |
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