Electronic Health Record (EHR) Data Processing and Analysis Tool
Add Lastdose Data
Statistical Analysis for PheWAS
Internal functions for buildDose process
PK oral functions
Combine Dose Data
Internal functions for collapseDose process
Collapse Dose Data
Data Transformation
dd.baseline
dd.baseline.small
dd
dd.small
Electronic Health Record (EHR) Data Processing and Analysis Tool
Internal functions for Extract-Med module
Extract medication information from clinical notes
Convert Character Frequency to Numeric
Create ID Crosswalk
Internal functions for lastdose
Firth's penalized-likelihood logistic regression with more decimal pla...
Make Dose Data
Internal functions for EHR modules
Internal functions for parse process
Parse CLAMP NLP Output
Parse MedEx NLP Output
Parse medExtractR NLP Output
Parse MedXN NLP Output
Internal functions for pk analysis
Process and standardize extracted last dose times
Pull Fake/Mod ID
Pull Real ID
Read and Transform
Build-PK-IV Module
Build-PK-Oral Module
Run Demographic Data
Run Drug Level Data
Run Lab Data
Run Str Data I
Run Structured E-Prescription Data
Standardize Dose Entity
Standardize Dose Change Entity
Standardize Dose Schedule Entity
Standardize Duration Entity
Standardize Frequency Entity
Standardize Route Entity
Standardize Strength Entity
Write Check File as CSV
Make Zero One Contingency Tables
Process and analyze electronic health record (EHR) data. The 'EHR' package provides modules to perform diverse medication-related studies using data from EHR databases. Especially, the package includes modules to perform pharmacokinetic/pharmacodynamic (PK/PD) analyses using EHRs, as outlined in Choi, Beck, McNeer, Weeks, Williams, James, Niu, Abou-Khalil, Birdwell, Roden, Stein, Bejan, Denny, and Van Driest (2020) <doi:10.1002/cpt.1787>. Additional modules will be added in future. In addition, this package provides various functions useful to perform Phenome Wide Association Study (PheWAS) to explore associations between drug exposure and phenotypes obtained from EHR data, as outlined in Choi, Carroll, Beck, Mosley, Roden, Denny, and Van Driest (2018) <doi:10.1093/bioinformatics/bty306>.