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.
- Version1.2.1
- R versionunknown
- LicenseGPL-3
- Needs compilation?No
- Last release09/17/2020
Documentation
Team
PARSE LTD
Chuan Hong
Show author detailsRolesAuthorYichi Zhang
Show author detailsRolesAuthorTianxi Cai
Show author detailsRolesAuthor
Insights
Last 30 days
This package has been downloaded 164 times in the last 30 days. Enough downloads to make a small wave in the niche community. The curiosity is spreading! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 5 times.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Last 365 days
This package has been downloaded 2,129 times in the last 365 days. Now we’re talking! This work is officially 'heard of in academic circles', just like those wild research papers on synthetic bananas. The day with the most downloads was Sep 11, 2024 with 28 downloads.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
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Dependencies
- Imports2 packages
- Suggests6 packages