Package: AutoScore
Type: Package
Title: An Interpretable Machine Learning-Based Automatic Clinical Score
        Generator
Version: 1.0.0
Date: 2022-10-08
Authors@R: c(person("Feng", "Xie", role = c("aut","cre"), comment = c(ORCID = "0000-0002-0215-667X"),
                    email= "xief@u.duke.nus.edu"),
             person("Yilin", "Ning", role = c("aut"), comment = c(ORCID = "0000-0002-6758-4472"),
                    email= "yilin.ning@duke-nus.edu.sg"),
             person("Han", "Yuan", role = c("aut"), comment = c(ORCID = "0000-0002-2674-6068"),
                    email= "yuan.han@u.duke.nus.edu"),
             person("Mingxuan", "Liu", role = c("aut"), comment = c(ORCID = "0000-0002-4274-9613"),
                    email= "e0572499@u.nus.edu"),
             person("Seyed Ehsan", "Saffari", role = c("aut"), comment = c(ORCID = "0000-0002-6473-4375"),
                    email = "ehsan.saffari@duke-nus.edu.sg"),
            person("Siqi", "Li", role = c("aut"), comment = c(ORCID = "0000-0002-1660-105X"),
                    email= "siqili@u.duke.nus.edu"),
             person("Bibhas", "Chakraborty", role = c("aut"), comment = c(ORCID = "0000-0002-7366-0478"),
                    email = "bibhas.chakraborty@duke-nus.edu.sg"),
             person("Nan", "Liu", role = c("aut"), comment = c(ORCID = "0000-0003-3610-4883"),
                    email = "liu.nan@duke-nus.edu.sg"))
URL: https://github.com/nliulab/AutoScore
BugReports: https://github.com/nliulab/AutoScore/issues
Description: 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.
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.2.1
Imports: tableone, pROC, randomForest, ggplot2, knitr, Hmisc, car,
        coxed, dplyr, ordinal, survival, tidyr, plotly, magrittr,
        randomForestSRC, rlang, survAUC, survminer
Depends: R (>= 3.5.0)
VignetteBuilder: knitr
Suggests: rpart, rmarkdown
NeedsCompilation: no
Packaged: 2022-10-14 18:06:47 UTC; xxxxf
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>
Repository: CRAN
Date/Publication: 2022-10-15 22:15:26 UTC
Built: R 4.2.0; ; 2023-07-11 03:23:48 UTC; unix
