A detailed study on temporal data visualization techniques in electronic health records

2016 ◽  
Author(s):  
Aisha Younas ◽  
Muhammad Sheraz Arhsad Malik ◽  
Khalil-ur-Rehman ◽  
Rabia Shahid
Keyword(s):  
Electronic Health Records ◽  
Data Visualization ◽  
Temporal Data ◽  
Health Records ◽  
Electronic Health ◽  
Visualization Techniques

scholarly journals Learning from heterogeneous temporal data in electronic health records

2017 ◽  
Vol 65 ◽  
pp. 105-119 ◽  
Author(s):  
Jing Zhao ◽  
Panagiotis Papapetrou ◽  
Lars Asker ◽  
Henrik Boström
Keyword(s):  
Electronic Health Records ◽  
Temporal Data ◽  
Health Records ◽  
Electronic Health

scholarly journals EHRtemporalVariability: delineating temporal data-set shifts in electronic health records

GigaScience ◽  
2020 ◽  
Vol 9 (8) ◽  
Author(s):  
Carlos Sáez ◽  
Alba Gutiérrez-Sacristán ◽  
Isaac Kohane ◽  
Juan M García-Gómez ◽  
Paul Avillach
Keyword(s):  
Electronic Health Records ◽  
R Package ◽  
Reliable Data ◽  
Data Reuse ◽  
Statistical Distributions ◽  
Temporal Data ◽  
Data Set ◽  
Health Records ◽  
Electronic Health ◽  
Over Time

Abstract Background Temporal variability in health-care processes or protocols is intrinsic to medicine. Such variability can potentially introduce dataset shifts, a data quality issue when reusing electronic health records (EHRs) for secondary purposes. Temporal data-set shifts can present as trends, as well as abrupt or seasonal changes in the statistical distributions of data over time. The latter are particularly complicated to address in multimodal and highly coded data. These changes, if not delineated, can harm population and data-driven research, such as machine learning. Given that biomedical research repositories are increasingly being populated with large sets of historical data from EHRs, there is a need for specific software methods to help delineate temporal data-set shifts to ensure reliable data reuse. Results EHRtemporalVariability is an open-source R package and Shiny app designed to explore and identify temporal data-set shifts. EHRtemporalVariability estimates the statistical distributions of coded and numerical data over time; projects their temporal evolution through non-parametric information geometric temporal plots; and enables the exploration of changes in variables through data temporal heat maps. We demonstrate the capability of EHRtemporalVariability to delineate data-set shifts in three impact case studies, one of which is available for reproducibility. Conclusions EHRtemporalVariability enables the exploration and identification of data-set shifts, contributing to the broad examination and repurposing of large, longitudinal data sets. Our goal is to help ensure reliable data reuse for a wide range of biomedical data users. EHRtemporalVariability is designed for technical users who are programmatically utilizing the R package, as well as users who are not familiar with programming via the Shiny user interface. Availability: https://github.com/hms-dbmi/EHRtemporalVariability/ Reproducible vignette: https://cran.r-project.org/web/packages/EHRtemporalVariability/vignettes/EHRtemporalVariability.html Online demo: http://ehrtemporalvariability.upv.es/

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