c(
person("Iker", "Rivas-González", , "ikerrivas96@gmail.com", role = c("aut", "cre"),
comment = c(ORCID = "0000-0002-0515-0628")),
person("Asger", "Hobolth", , "asger@math.au.dk", role = "aut"),
person("Lars Nørvang", "Andersen", , "larsa@math.au.dk", role = "aut"),
person("Colin", "Guetemme", , "colin.guetemme@live.fr", role = "aut")
)
Iker Rivas-González [aut, cre]
(<https://orcid.org/0000-0002-0515-0628>),
Asger Hobolth [aut],
Lars Nørvang Andersen [aut],
Colin Guetemme [aut]

High-Throughput Phenotyping with EHR using a Common Automated
Pipeline
c(
person("Yichi", "Zhang", role = "aut"),
person("Chuan", "Hong", role = "aut"),
person("Tianxi", "Cai", role = "aut"),
person(family = "PARSE LTD", role =  c("aut", "cre"), email = "software@parse-health.org")
)
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>.
graphics, methods, stats, utils, glmnet, RMySQL
ggplot2, e1071, randomForestSRC, xgboost, knitr
Yichi Zhang [aut],
Chuan Hong [aut],
Tianxi Cai [aut],
PARSE LTD [aut, cre]

ggplot2, e1071, randomForestSRC, xgboost, knitr, rmarkdown

Unsupervised Gold-Standard Label Free Phenotyping Algorithm for
EHR Data
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")
)
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>.
Sheng Yu [aut],
Victor Castro [aut],
Clara-Lea Bonzel [aut, cre],
Molei Liu [aut],
Chuan Hong [aut],
Tianxi Cai [aut],
PARSE LTD [aut]