Functions to delineate temporal dataset shifts in Electronic Health
Records through the projection and visualization of dissimilarities
among data temporal batches. This is done through the estimation of
data statistical distributions over time and their projection in
non-parametric statistical manifolds, uncovering the patterns of the
data latent temporal variability. 'EHRtemporalVariability' is
particularly suitable for multi-modal data and categorical variables
with a high number of values, common features of biomedical data where
traditional statistical process control or time-series methods may not
be appropriate. 'EHRtemporalVariability' allows you to explore and
identify dataset shifts through visual analytics formats such as
Data Temporal heatmaps and Information Geometric Temporal (IGT) plots.
An additional 'EHRtemporalVariability' Shiny app can be used to load
and explore the package results and even to allow the use of these
functions to those users non-experienced in R coding. (Sáez et al. 2020)
<doi:10.1093/gigascience/giaa079>.
| Version: |
1.1.4 |
| Depends: |
R (≥ 3.3.0), dplyr |
| Imports: |
plotly, zoo, xts, lubridate, RColorBrewer, viridis, scales, methods, MASS |
| Suggests: |
knitr, rmarkdown, devtools, BiocStyle, dbscan, webshot |
| Published: |
2021-05-31 |
| Author: |
Carlos Sáez [aut, cre],
Alba Gutiérrez-Sacristán [aut],
Isaac Kohane [aut],
Juan M García-Gómez [aut],
Paul Avillach [aut],
Biomedical Data Science Lab, Universitat Politècnica de València
(Spain) [cph],
Department of Biomedical Informatics, Harvard Medical School [cph] |
| Maintainer: |
Carlos Sáez <carsaesi at upv.es> |
| License: |
Apache License 2.0 | file LICENSE |
| URL: |
https://github.com/hms-dbmi/EHRtemporalVariability |
| NeedsCompilation: |
no |
| Materials: |
README NEWS |
| CRAN checks: |
EHRtemporalVariability results |