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EHRtemporalVariability: Delineating Temporal Dataset Shifts in Electronic Health Records
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>.
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Package details |
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| 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@upv.es> |
| License | Apache License 2.0 | file LICENSE |
| Version | 1.1.4 |
| URL | https://github.com/hms-dbmi/EHRtemporalVariability |
| Package repository | View on CRAN |
| Installation |
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