High-Throughput Phenotyping with EHR using a Common Automated Pipeline
tools:::Rd_package_title("PheCAP")
Generate a Dictionary File for Note Parsing
Perform Majority Voting on the CUIs from Multiple Knowledge Sources
Plot ROC and Related Curves for Phenotyping Models
Predict Phenotype
Run Surrogate-Assisted Feature Extraction (SAFE)
Train Phenotyping Model using the Training Labels
Validate the Phenotyping Model using the Validation Labels
Define or Read Datasets for Phenotyping
Define a Surrogate Variable used in Surrogate-Assisted Feature Extract...
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>.
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