PheCAP
High-Throughput Phenotyping with EHR using a Common Automated Pipeline
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>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.2.1 |
rolling linux/jammy R-4.5 | PheCAP_1.2.1.tar.gz |
3.7 MiB |
1.2.1 |
rolling linux/noble R-4.5 | PheCAP_1.2.1.tar.gz |
3.7 MiB |
1.2.1 |
rolling source/ R- | PheCAP_1.2.1.tar.gz |
2.8 MiB |
1.2.1 |
latest linux/jammy R-4.5 | PheCAP_1.2.1.tar.gz |
3.7 MiB |
1.2.1 |
latest linux/noble R-4.5 | PheCAP_1.2.1.tar.gz |
3.7 MiB |
1.2.1 |
latest source/ R- | PheCAP_1.2.1.tar.gz |
2.8 MiB |
1.2.1 |
2026-04-26 source/ R- | PheCAP_1.2.1.tar.gz |
2.8 MiB |
1.2.1 |
2026-04-23 source/ R- | PheCAP_1.2.1.tar.gz |
2.8 MiB |
1.2.1 |
2026-04-09 windows/windows R-4.5 | PheCAP_1.2.1.zip |
3.7 MiB |
1.2.1 |
2025-04-20 source/ R- | PheCAP_1.2.1.tar.gz |
2.8 MiB |