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hms-dbmi
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>.
Maintained by Carlos Sáez. Last updated 11 months ago.
biomedical-data-sciencebiomedical-informaticsdata-qualitydata-quality-monitoringdataset-shiftselectronic-health-recordstimevariabilityvisualization
38.5 match 17 stars 5.27 score 22 scripts
marksendak
constellation:Identify Event Sequences Using Time Series Joins
Examine any number of time series data frames to identify instances in which various criteria are met within specified time frames. In clinical medicine, these types of events are often called "constellations of signs and symptoms", because a single condition depends on a series of events occurring within a certain amount of time of each other. This package was written to work with any number of time series data frames and is optimized for speed to work well with data frames with millions of rows.
Maintained by Mark Sendak. Last updated 6 years ago.
electronic-health-recordelectronic-health-recordshealthcarepatientstimeseries
40.0 match 6 stars 4.76 score 19 scripts