AutoScore

AutoScore: An Interpretable Machine Learning-Based Automatic Clinical Score Generator

https://github.com/nliulab/autoscore

Science Score: 46.0%

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Found 21 DOI reference(s) in README
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2 of 5 committers (40.0%) from academic institutions
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Low similarity (10.2%) to scientific vocabulary

Last synced: 7 months ago · JSON representation

Repository

AutoScore: An Interpretable Machine Learning-Based Automatic Clinical Score Generator

Basic Info

Host: GitHub
Owner: nliulab
Language: R
Default Branch: master
Homepage:
Size: 11.6 MB

Statistics

Stars: 33
Watchers: 3
Forks: 5
Open Issues: 13
Releases: 0

Created over 6 years ago · Last pushed 9 months ago

Metadata Files

Readme

README.md

AutoScore: An Interpretable Machine Learning-Based Automatic Clinical

Score Generator

AutoScore is a novel machine learning framework to automate the development of interpretable clinical scoring models. AutoScore consists of six modules: 1) variable ranking with machine learning, 2) variable transformation, 3) score derivation, 4) model selection, 5) domain knowledge-based score fine-tuning, and 6) performance evaluation. The original AutoScore structure is elaborated in this article and its flowchart is shown in the following figure. AutoScore was originally designed for binary outcomes and later extended to survival outcomes and ordinal outcomes. AutoScore could seamlessly generate risk scores using a parsimonious set of variables for different types of clinical outcomes, which can be easily implemented and validated in clinical practice. Moreover, it enables users to build transparent and interpretable clinical scores quickly in a straightforward manner.

Please visit our bookdown page for a full tutorial on AutoScore usage.

Usage

The five pipeline functions constitute the 5-step AutoScore-based process for generating point-based clinical scores for binary, survival and ordinal outcomes.

This 5-step process gives users the flexibility of customization (e.g., determining the final list of variables according to the parsimony plot, and fine-tuning the cutoffs in variable transformation):

STEP(i): AutoScore_rank()or AutoScore_rank_Survival() or AutoScore_rank_Ordinal() - Rank variables with machine learning (AutoScore Module 1)
STEP(ii): AutoScore_parsimony() or AutoScore_parsimony_Survival() or AutoScore_parsimony_Ordinal() - Select the best model with parsimony plot (AutoScore Modules 2+3+4)
STEP(iii): AutoScore_weighting() or AutoScore_weighting_Survival() or AutoScore_weighting_Ordinal() - Generate the initial score with the final list of variables (Re-run AutoScore Modules 2+3)
STEP(iv): AutoScore_fine_tuning() or AutoScore_fine_tuning_Survival() or AutoScore_fine_tuning_Ordinal() - Fine-tune the score by revising cut_vec with domain knowledge (AutoScore Module 5)
STEP(v): AutoScore_testing() or AutoScore_testing_Survival() or AutoScore_testing_Ordinal() - Evaluate the final score with ROC analysis (AutoScore Module 6)

We also include several optional functions in the package, which could help with data analysis and result reporting.

Citation

Core paper

Xie F, Chakraborty B, Ong MEH, Goldstein BA, Liu N. AutoScore: A machine learning-based automatic clinical score generator and its application to mortality prediction using electronic health records. JMIR Medical Informatics 2020; 8(10): e21798.
Xie F, Ning Y, Liu M, Li S, Saffari SE, Yuan H, Volovici V, Ting DSW, Goldstein BA, Ong MEH, Vaughan R, Chakraborty B, Liu N. A universal AutoScore framework to develop interpretable scoring systems for predicting common types of clinical outcomes. STAR Protocols 2023 Jun; 4(2): 102302.

Method extension

Xie F, Ning Y, Yuan H, Goldstein BA, Ong MEH, Liu N, Chakraborty B. AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data. Journal of Biomedical Informatics 2022; 125: 103959.
Saffari SE, Ning Y, Xie F, Chakraborty B, Volovici V, Vaughan R, Ong MEH, Liu N, AutoScore-Ordinal: An interpretable machine learning framework for generating scoring models for ordinal outcomes, BMC Medical Research Methodology 2022; 22: 286.
Ning Y, Li S, Ong ME, Xie F, Chakraborty B, Ting DS, Liu N. A novel interpretable machine learning system to generate clinical risk scores: An application for predicting early mortality or unplanned readmission in a retrospective cohort study. PLOS Digital Health 2022; 1(6): e0000062.

Clinical application

This page provides a collection of clinical applications using AutoScore and its extensions. The application list is categorized according to medical specialties and is updated regularly. However, due to the manual process of updating, we are unable to keep track of all publications.

Contact

Feng Xie (Email: xief@u.duke.nus.edu)
Yilin Ning (Email: yilin.ning@duke-nus.edu.sg)
Nan Liu (Email: liu.nan@duke-nus.edu.sg)

Package installation

Install from GitHub or CRAN:

``` r

From Github

install.packages("devtools") library(devtools) installgithub(repo = "nliulab/AutoScore", buildvignettes = TRUE)

From CRAN (recommended)

install.packages("AutoScore") ```

Load AutoScore package:

r library(AutoScore)

Owner

Login: nliulab
Kind: user
Location: Singapore
Company: Duke-NUS Medical School

Website: http://www.DigitalMedicineLab.org/
Twitter: nliulab
Repositories: 3
Profile: https://github.com/nliulab

LIU Lab - Digital Medicine

GitHub Events

Total

Watch event: 2
Push event: 14
Fork event: 1

Last Year

Watch event: 2
Push event: 14
Fork event: 1

Committers

Last synced: over 2 years ago

All Time

Total Commits: 222
Total Committers: 5
Avg Commits per committer: 44.4
Development Distribution Score (DDS): 0.541

Past Year

Commits: 25
Committers: 2
Avg Commits per committer: 12.5
Development Distribution Score (DDS): 0.12

Top Committers

Name	Email	Commits
nliulab	5****b	102
XIE FENG	x**f@u**u	97
nyilin	n**l@g**m	20
siqili0325	s**i@u**u	2
Michelle Liu	5****n	1

Committer Domains (Top 20 + Academic)

umich.edu: 1 u.duke.nus.edu: 1

Issues and Pull Requests

Last synced: over 2 years ago

All Time

Total issues: 9
Total pull requests: 1
Average time to close issues: N/A
Average time to close pull requests: about 15 hours
Total issue authors: 5
Total pull request authors: 1
Average comments per issue: 2.22
Average comments per pull request: 0.0
Merged pull requests: 1
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 6
Pull requests: 0
Average time to close issues: N/A
Average time to close pull requests: N/A
Issue authors: 2
Pull request authors: 0
Average comments per issue: 2.5
Average comments per pull request: 0
Merged pull requests: 0
Bot issues: 0
Bot pull requests: 0

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Top Authors

Issue Authors

shlid007 (4)
renlok (1)
Aimusa (1)
EvaPang2022 (1)
mythhere99 (1)

Pull Request Authors

fengx13 (1)

Top Labels

Issue Labels

Pull Request Labels

Packages

Total packages: 1
Total downloads:
- cran 495 last-month

Total dependent packages: 0
Total dependent repositories: 0
Total versions: 4
Total maintainers: 1

cran.r-project.org: AutoScore

An Interpretable Machine Learning-Based Automatic Clinical Score Generator

Homepage: https://github.com/nliulab/AutoScore
Documentation: http://cran.r-project.org/web/packages/AutoScore/AutoScore.pdf
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
Latest release: 1.1.0
published 8 months ago

Versions: 4
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 495 Last month

Rankings

Stargazers count: 11.9%

Forks count: 12.8%

Average: 25.1%

Dependent packages count: 29.8%

Downloads: 35.3%

Dependent repos count: 35.5%

Maintainers (1)

xief@u.duke.nus.edu

Last synced: 8 months ago

Dependencies

DESCRIPTION cran

R >= 3.5.0 depends
Hmisc * imports
car * imports
coxed * imports
dplyr * imports
ggplot2 * imports
knitr * imports
magrittr * imports
ordinal * imports
pROC * imports
plotly * imports
randomForest * imports
randomForestSRC * imports
rlang * imports
survAUC * imports
survival * imports
survminer * imports
tableone * imports
tidyr * imports
rmarkdown * suggests
rpart * suggests