Package: MAP
Type: Package
Title: Multimodal Automated Phenotyping
Version: 1.0.0
Authors@R: c(
    person("Thomas", "Charlon", role = c("aut", "cre"),
           email = "charlon@protonmail.com",
           comment = c(ORCID = "0000-0001-7497-0470")),
    person("Chuan", "Hong", role = "aut"),
    person("Jiehuan", "Sun", role = "aut"),
    person("Katherine", "Liao", role = "aut"),
    person("Sheng", "Yu", role = "aut"),
    person("Tianxi", "Cai", role = "aut"),
    person("PARSE Health", role = "aut", comment = "<https://parse-health.org>"),
    person("CELEHS", role = "aut", comment = "<https://celehs.hms.harvard.edu>")
    )
Description: Electronic health records (EHR) linked with
        biorepositories are a powerful platform for translational
        studies. A major bottleneck exists in the ability to phenotype
        patients accurately and efficiently. Towards that end, we
        developed an automated high-throughput phenotyping method
        integrating International Classification of Diseases (ICD)
        codes and narrative data extracted using natural language
        processing (NLP). Specifically, our proposed method, called MAP
        (Map Automated Phenotyping algorithm), fits an ensemble of
        latent mixture models on aggregated ICD and NLP counts along
        with healthcare utilization. The MAP algorithm yields a
        predicted probability of phenotype for each patient and a
        threshold for classifying subjects with phenotype yes/no (See
        Katherine P. Liao, et al. (2019) <doi:10.1093/jamia/ocz066>.).
URL: https://github.com/celehs/MAP, https://celehs.github.io/MAP/
BugReports: https://github.com/celehs/MAP/issues
License: GPL-3
Encoding: UTF-8
LazyData: true
Depends: R (>= 3.4.0), flexmix (>= 2.3-14), Matrix(>= 1.2-10)
Imports: magrittr
Suggests: knitr, pROC, rmarkdown, testthat
VignetteBuilder: knitr
NeedsCompilation: no
RoxygenNote: 7.3.2
Repository: https://celehs.r-universe.dev
Date/Publication: 2025-01-08 21:27:10 UTC
RemoteUrl: https://github.com/celehs/map
RemoteRef: HEAD
RemoteSha: 0361d0424cabadae38781f9f8f9d76be2e132d84
Packaged: 2026-07-03 23:57:51 UTC; root
Author: Thomas Charlon [aut, cre] (ORCID:
    <https://orcid.org/0000-0001-7497-0470>),
  Chuan Hong [aut],
  Jiehuan Sun [aut],
  Katherine Liao [aut],
  Sheng Yu [aut],
  Tianxi Cai [aut],
  PARSE Health [aut] (<https://parse-health.org>),
  CELEHS [aut] (<https://celehs.hms.harvard.edu>)
Maintainer: Thomas Charlon <charlon@protonmail.com>