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PheNorm

https://celehs.github.io/PheNorm/

https://github.com/celehs/phenorm

Science Score: 23.0%

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

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    CITATION.cff file
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    codemeta.json file
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    .zenodo.json file
  • ✓
    DOI references
    Found 1 DOI reference(s) in README
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    Academic publication links
    Links to: ncbi.nlm.nih.gov
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    Scientific vocabulary similarity
    Low similarity (9.8%) to scientific vocabulary
Last synced: 11 months ago · JSON representation

Repository

https://celehs.github.io/PheNorm/

Basic Info
  • Host: GitHub
  • Owner: celehs
  • Language: R
  • Default Branch: master
  • Homepage:
  • Size: 85 KB
Statistics
  • Stars: 5
  • Watchers: 2
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created almost 6 years ago · Last pushed over 5 years ago
Metadata Files
Readme

README.Rmd 

---
output: github_document
---

# PheNorm: Unsupervised Gold-Standard Label Free Phenotyping Algorithm for EHR Data

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

## Overview

The PheNorm R package provides an unsupervised phenotyping algorithm, for electronic health record (EHR) data. A human-annotated training set with gold-standard disease status labels is usually required to build an algorithm for phenotyping based on a set of predictive features. PheNorm, however, does not require expert-labeled samples for training.

The algorithm combines the most predictive variables, such as the counts of the main International Classification of Diseases (ICD) codes, with other EHR features. Those include for example health utilization and processed clinical note data. PheNorm aims to obtain a score for accurate risk prediction and disease classification. In particular, it normalizes the surrogate to resemble Gaussian mixture and leverages the remaining features through random corruption denoising. PheNorm automatically generates phenotyping algorithms and demonstrates the capacity for EHR-driven annotations to scale to the next level phenotypic big data.

The input data consists of ICD codes and additional features.

The PheNorm output includes: 

- the predicted probability of the risk of having the phenotype 

- the coefficient beta corresponding to all the features additional to the ICD codes.

The main steps of the algorithm are presented in the following flowchart:

![](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6251688/bin/ocx111f1.jpg)

## Installation

Install stable version from CRAN:

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

Install development version from GitHub:

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

## Citation

Yu S, Ma Y, Gronsbell J, Cai T, Ananthakrishnan AN, Gainer VS, Churchill SE, Szolovits P, Murphy SN, Kohane IS, Liao KP, Cai T. Enabling phenotypic big data with PheNorm. J Am Med Inform Assoc. 2018 Jan 1;25(1):54-60. doi: 10.1093/jamia/ocx111. PMID: 29126253; PMCID: PMC6251688. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6251688/

Owner

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

Translational Data Science Center for a Learning Health System

GitHub Events

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Last Year

Committers

Last synced: almost 3 years ago

All Time
  • Total Commits: 24
  • Total Committers: 3
  • Avg Commits per committer: 8.0
  • Development Distribution Score (DDS): 0.25
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Clara-Lea c****l@h****r 18
biostatstudio h****g@g****m 5
Translational Data Science Center for a Learning Health System 4****s 1

Issues and Pull Requests

Last synced: over 2 years ago

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  • Total issues: 0
  • Total pull requests: 0
  • Average time to close issues: N/A
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  • Total issue authors: 0
  • Total 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
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
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  • Average comments per issue: 0
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  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
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Packages

  • Total packages: 1
  • Total downloads:
    • cran 223 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 1
  • Total maintainers: 1
cran.r-project.org: PheNorm

Unsupervised Gold-Standard Label Free Phenotyping Algorithm for EHR Data

  • Versions: 1
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 223 Last month
Rankings
Forks count: 21.9%
Stargazers count: 22.5%
Dependent packages count: 29.8%
Dependent repos count: 35.5%
Average: 39.4%
Downloads: 87.6%
Maintainers (1)
clbonzel@hsph.harvard.edu
Last synced: 12 months ago

Dependencies

DESCRIPTION cran
  • knitr * suggests
  • rmarkdown * suggests
  • testthat * suggests