Research and Exploratory Analysis Driven—Time-data Visualization (read-tv) software. Issue 1 (1st March 2021)
- Record Type:
- Journal Article
- Title:
- Research and Exploratory Analysis Driven—Time-data Visualization (read-tv) software. Issue 1 (1st March 2021)
- Main Title:
- Research and Exploratory Analysis Driven—Time-data Visualization (read-tv) software
- Authors:
- Del Gaizo, John
Catchpole, Ken R
Alekseyenko, Alexander V - Abstract:
- Abstract: Motivation: Research & Exploratory Analysis Driven Time-data Visualization ( read-tv ) is an open source R Shiny application for visualizing irregularly and regularly spaced longitudinal data. read-tv provides unique filtering and changepoint analysis (CPA) features. The need for these analyses was motivated by research of surgical work-flow disruptions in operating room settings. Specifically, for the analysis of the causes and characteristics of periods of high disruption-rates, which are associated with adverse surgical outcomes. Materials and Methods: read-tv is a graphical application, and the main component of a package of the same name. read-tv generates and evaluates code to filter and visualize data. Users can view the visualization code from within the application, which facilitates reproducibility. The data input requirements are simple, a table with a time column with no missing values. The input can either be in the form of a file, or an in-memory dataframe– which is effective for rapid visualization during curation. Results: We used read-tv to automatically detect surgical disruption cascades. We found that the most common disruption type during a cascade was training, followed by equipment. Discussion: read-tv fills a need for visualization software of surgical disruptions and other longitudinal data. Every visualization is reproducible, the exact source code that read-tv executes to create a visualization is available from within the application.Abstract: Motivation: Research & Exploratory Analysis Driven Time-data Visualization ( read-tv ) is an open source R Shiny application for visualizing irregularly and regularly spaced longitudinal data. read-tv provides unique filtering and changepoint analysis (CPA) features. The need for these analyses was motivated by research of surgical work-flow disruptions in operating room settings. Specifically, for the analysis of the causes and characteristics of periods of high disruption-rates, which are associated with adverse surgical outcomes. Materials and Methods: read-tv is a graphical application, and the main component of a package of the same name. read-tv generates and evaluates code to filter and visualize data. Users can view the visualization code from within the application, which facilitates reproducibility. The data input requirements are simple, a table with a time column with no missing values. The input can either be in the form of a file, or an in-memory dataframe– which is effective for rapid visualization during curation. Results: We used read-tv to automatically detect surgical disruption cascades. We found that the most common disruption type during a cascade was training, followed by equipment. Discussion: read-tv fills a need for visualization software of surgical disruptions and other longitudinal data. Every visualization is reproducible, the exact source code that read-tv executes to create a visualization is available from within the application. read-tv is generalizable, it can plot any tabular dataset given the simple requirements that there is a numeric, datetime, or datetime string column with no missing values. Finally, the tab-based architecture of read-tv is easily extensible, it is relatively simple to add new functionality by implementing a tab in the source code. Conclusion: read-tv enables quick identification of patterns through customizable longitudinal plots; faceting; CPA; and user-specified filters. The package is available on GitHub under an MIT license. … (more)
- Is Part Of:
- JAMIA open. Volume 4:Issue 1(2021)
- Journal:
- JAMIA open
- Issue:
- Volume 4:Issue 1(2021)
- Issue Display:
- Volume 4, Issue 1 (2021)
- Year:
- 2021
- Volume:
- 4
- Issue:
- 1
- Issue Sort Value:
- 2021-0004-0001-0000
- Page Start:
- Page End:
- Publication Date:
- 2021-03-01
- Subjects:
- longitudinal visualization -- change-point analysis -- change point analysis -- changepoint analysis -- forecasting -- R -- Shiny -- surgical safety
Medical informatics -- Periodicals
610.285 - Journal URLs:
- http://www.oxfordjournals.org/ ↗
https://academic.oup.com/jamiaopen ↗ - DOI:
- 10.1093/jamiaopen/ooab007 ↗
- Languages:
- English
- ISSNs:
- 2574-2531
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 15962.xml