High-Throughput Phenotyping with EHR using a Common Automated Pipeline
Overview
Implement surrogate-assisted feature extraction (SAFE) and common machine learning approaches to train and validate phenotyping models. Background and details about the methods can be found at Zhang et al. (2019) doi:10.1038/s41596-019-0227-6, Yu et al. (2017) doi:10.1093/jamia/ocw135, and Liao et al. (2015) doi:10.1136/bmj.h1885.
Install
Health
- OK13 OK2026-09-08
- ERROR12 OK, 1 ERROR2026-09-07
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK12 OK2026-04-25
Show 1 earlier changeHide 1 earlier change
- NOTE13 OK, 1 NOTE2026-03-10
Documentation
- Help pages
- 11includes a package overview page
- Examples that run
- 100%1 run in a plain check
- Exported functions with examples
- 0%
- Documented parameters
- 100%
- Return-value docs
- 89%
- Help pages citing references
- 9%
- Example 1: Simulated Data HTML
- Example 2: Real EHR Data HTML
- Main Steps HTML
- NER using MetaMAP HTML
- Running NLP using NILE HTML
Downloads
Repository
Stars over time
Issues over time
4 development-tooling and community-health practices detected across 2 families in the upstream repository
Checks run against github.com/celehs/phecap on 2026-10-18.
Dependencies
Code & Tests
Datasets
People & History
Pick two releases to compare their code metrics.
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- RR 4.3.0 released · 2023-04-21
- RR 4.2.0 released · 2022-04-22
- RR 4.1.0 released · 2021-05-18
- unarchivedReturned to CRAN2020-09-17
- 1.2.1Latest
- archivedRemoved from CRAN2020-05-29check problems were not corrected in time
- 1.1.02020-05-02
- RR 4.0.0 released · 2020-04-24
Package metadata
- First published
- 2020-05-02
- Total releases
- 2 / 6 yrs
- License
- GPL-3 OSI
- Minimum R
- ≥ 3.3.0
- Bundled data
- 1.5 MB / 2 files
- Download size
- 2.9 MB
Cite
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