Package: PheNorm
Type: Package
Title: Unsupervised Gold-Standard Label Free Phenotyping Algorithm for
        EHR Data
Version: 0.1.1
Authors@R: c(
    person("Sheng", "Yu", role = "aut"),
    person("Victor", "Castro", role = "aut"),
    person("Clara-Lea", "Bonzel", role = c("aut", "cre"), email = "clbonzel@hsph.harvard.edu"),
    person("Molei", "Liu", role = "aut"),
    person("Chuan", "Hong", role = "aut"),
    person("Tianxi", "Cai", role = "aut"),
    person(family = "PARSE LTD", role = "aut", email = "software@parse-health.org")
    )
Description: The algorithm combines the most predictive variable, such
        as count of the main International Classification of Diseases
        (ICD) codes, and other Electronic Health Record (EHR) features
        (e.g. health utilization and processed clinical note data), to
        obtain a score for accurate risk prediction and disease
        classification. In particular, it normalizes the surrogate to
        resemble gaussian mixture and leverages the remaining features
        through random corruption denoising. Background and details
        about the method can be found at Yu et al. (2018)
        <doi:10.1093/jamia/ocx111>.
License: GPL-3
Encoding: UTF-8
RoxygenNote: 7.1.1
URL: https://celehs.github.io/PheNorm/
BugReports: https://github.com/celehs/PheNorm/issues
Suggests: knitr, rmarkdown, testthat
VignetteBuilder: knitr
Repository: https://celehs.r-universe.dev
Date/Publication: 2021-02-23 06:08:30 UTC
RemoteUrl: https://github.com/celehs/phenorm
RemoteRef: HEAD
RemoteSha: 7ea2ecee5b4ce88c029eee22341cc5536167effc
NeedsCompilation: no
Packaged: 2026-06-18 10:15:47 UTC; root
Author: Sheng Yu [aut],
  Victor Castro [aut],
  Clara-Lea Bonzel [aut, cre],
  Molei Liu [aut],
  Chuan Hong [aut],
  Tianxi Cai [aut],
  PARSE LTD [aut]
Maintainer: Clara-Lea Bonzel <clbonzel@hsph.harvard.edu>