Data

Tools

Indexes

Applications

Experiments

Open Source Science

scornet

https://celehs.github.io/SCORNET/

https://github.com/celehs/scornet

Science Score: 23.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ○
    codemeta.json file
  • ○
    .zenodo.json file
  • ✓
    DOI references
    Found 1 DOI reference(s) in README
  • ○
    Academic publication links
  • ✓
    Committers with academic emails
    1 of 3 committers (33.3%) from academic institutions
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (9.0%) to scientific vocabulary
Last synced: 11 months ago · JSON representation

Repository

https://celehs.github.io/SCORNET/

Basic Info
  • Host: GitHub
  • Owner: celehs
  • License: gpl-3.0
  • Language: R
  • Default Branch: master
  • Homepage:
  • Size: 2.82 MB
Statistics
  • Stars: 3
  • Watchers: 3
  • Forks: 1
  • Open Issues: 0
  • Releases: 0
Created almost 6 years ago · Last pushed over 1 year ago
Metadata Files
Readme License

README.Rmd 

---
output: github_document
---

# SCORNET: Semi-Supervised Calibration of Risk with Noisy Event Times

[![CRAN](https://www.r-pkg.org/badges/version/SCORNET)](https://CRAN.R-project.org/package=SCORNET)

## Overview

The Semi-supervised Calibration of Risk with Noisy Event Times (SCORNET) is a consistent, semi-supervised, non-parametric survival curve estimator optimized for efficient use of Electronic Health Record (EHR) data with a limited number of current status labels. Derived from van der Laan and Robins' Inverse Probability of Censoring Weighted (IPCW) estimator, it achieves locally efficient survival curve estimation using current status labels -- binary indicators of phenotype status at censoring time -- rather than more expensive event time labels. SCORNET boosts efficiency over IPCW in the typical EHR setting by (1) utilizing unlabeled patients in a semi-supervised fashion, and (2) leveraging information-dense engineered EHR features to maximize imputation precision in the unlabeled set.

![Schematic of the SCORNET algorithm.](https://github.com/celehs/SCORNET/blob/master/img/scornet_flowchart.png?raw=true)

See Ahuja et al. (2020) for details.

## Installation

Install stable version from CRAN:

```{r, eval=FALSE}
install.packages("SCORNET")
```

Install development version from GitHub:

```{r, eval=FALSE}
# install.packages("remotes")
remotes::install_github("celehs/SCORNET")
```

## References

- Ahuja Y, Liang L, Huang S, Liao K, Cai T (2020). Semi-supervised Calibration of Risk with Noisy Event Times (SCORNET) Using Electronic Health Record Data. BioArxiv.

- Mark J. van der Laan & James M. Robins (1998) Locally Efficient Estimation with Current Status Data and Time-Dependent Covariates, Journal of the American Statistical Association, 93:442, 693-701, DOI: 10.1080/01621459.1998.10473721

Owner

  • Name: CELEHS
  • Login: celehs
  • Kind: user
  • Location: Boston, USA

Translational Data Science Center for a Learning Health System

GitHub Events

Total
  • Push event: 1
Last Year
  • Push event: 1

Committers

Last synced: over 3 years ago

All Time
  • Total Commits: 25
  • Total Committers: 3
  • Avg Commits per committer: 8.333
  • Development Distribution Score (DDS): 0.48
Top Committers
Name Email Commits
Ming Yang h****g@g****m 13
Yuri y****a@h****u 11
Translational Data Science Center for a Learning Health System 4****s@u****m 1
Committer Domains (Top 20 + Academic)
hms.harvard.edu: 1

Issues and Pull Requests

Last synced: 11 months ago

All Time
  • Total issues: 0
  • Total pull requests: 1
  • Average time to close issues: N/A
  • Average time to close pull requests: 1 minute
  • Total issue authors: 0
  • Total pull request authors: 1
  • Average comments per issue: 0
  • Average comments per pull request: 0.0
  • Merged pull requests: 1
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
Pull Request Authors
  • celehs (1)
Top Labels
Issue Labels
Pull Request Labels

Packages

  • Total packages: 1
  • Total downloads:
    • cran 226 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 2
  • Total maintainers: 1
cran.r-project.org: SCORNET

Semi-Supervised Calibration of Risk with Noisy Event Times

  • Versions: 2
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 226 Last month
Rankings
Forks count: 21.9%
Stargazers count: 26.2%
Dependent packages count: 29.8%
Dependent repos count: 35.5%
Average: 39.2%
Downloads: 82.8%
Maintainers (1)
Yuri_Ahuja@hms.harvard.edu
Last synced: 11 months ago

Dependencies

DESCRIPTION cran
  • Matrix * imports
  • Rcpp * imports
  • doParallel * imports
  • foreach * imports
  • parallel * imports
  • pracma * imports
  • survival * imports
  • knitr * suggests
  • rmarkdown * suggests