scholarly journals 4474 READ-TV: Research and Exploratory Analysis Driven Time-data Visualization

2020 ◽  
Vol 4 (s1) ◽  
pp. 51-51
Author(s):  
John Del Gaizo ◽  
Alexander Alekseyenko ◽  
Kenneth Catchpole
Keyword(s):  
Data Visualization ◽  
Change Point ◽  
Web Application ◽  
Exploratory Analysis ◽  
Change Point Analysis ◽  
List Type ◽  
Event Analysis ◽  
Time Data ◽  
User Friendliness ◽  
Changepoint Analysis

OBJECTIVES/GOALS: A web interface that allows for easy upload of CSV text data to time-based visualizationsImplementation of change points analysis to identify and display points where event rates increased or decreasedcustomizable plots where the user can change point shapes, color, etc.customizable and advanced filtering supportsupport for plot comparisons and exportsMETHODS/STUDY POPULATION: We used the R/Shiny framework to develop a web application for visualization of time stamped data. The Research and Exploratory Analysis Driven Time-data Visualization (READ-TV) application allows for user-friendly mining for longitudinal patterns in data. READ-TV is built specifically for FD analysis, but is easily adaptable to other clinical use cases, as we allow for the use of general metadata on events and cases.The building of a quantitative framework for event analysis starts with the application of homogeneous Poisson processes, which relate the times of occurrence of events in terms of an underlying rate. To understand the changes in this underlying rate, changepoint analysis is used to model the rate as a function of time using piecewise constant approximations. The changepoint analysis allows us to identify the specific periods of time where the rate of FD is increased relative to a baseline or a desired operating range. RESULTS/ANTICIPATED RESULTS: READ-TV application allows for import of time stamped event data from multiple cases. Event and case metadata are supported to facilitate filtering and mining of interesting subsets of data. Stem plots are used for visualization of selected event timelines in chosen cases. This visualization is accompanied with summary of the number and estimates of rates of occurrence of specific event types (e.g. types of FD). Change-point analysis is implemented using the ‘changepoint‘ R library. These analyses allow the users to quickly understand whether the rates of events (FD) is changing across the case timeline and where exactly these changes are occurring. DISCUSSION/SIGNIFICANCE OF IMPACT: We have demonstrated the READ-TV application to the team of the AHRQ-funded Human Factors and Systems Integration in High Technology Surgery (HF-SIgHTS) study. The ability to visualize and perform quantitative analysis of the study data was received with unanimous positive feedback and enthusiasm. We continue READ-TV development focusing on (1) increased user-friendliness using the HF-SIgHTS as our focus group, (2) increased functionality, and (3) use of more general localization terminology to allow for other applications.

scholarly journals Research and Exploratory Analysis Driven—Time-data Visualization (read-tv) software

JAMIA Open ◽  
2021 ◽  
Vol 4 (1) ◽  
Author(s):  
John Del Gaizo ◽  
Ken R Catchpole ◽  
Alexander V Alekseyenko
Keyword(s):  
Longitudinal Data ◽  
Data Visualization ◽  
Missing Values ◽  
Source Code ◽  
Exploratory Analysis ◽  
Time Data ◽  
Visualization Software ◽  
R Shiny ◽  
Changepoint Analysis ◽  
Main Component

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.

scholarly journals Web Application for Real Time Data Visualization of Heat Sensor

2018 ◽  
Author(s):  
Bernabe Ortega-Tenezaca ◽  
Viviana Quevedo-Tumailli ◽  
Víctor Cerda Mejía ◽  
Octavio Guijarro Rubí ◽  
Estela Guardado Yordi ◽  
...  
Keyword(s):  
Real Time ◽  
Data Visualization ◽  
Web Application ◽  
Time Data ◽  
Real Time Data ◽  
Heat Sensor

Application of Change Point Analysis of Response Time Data to Detect Test Speededness

2021 ◽  
pp. 001316442110463
Author(s):  
Ying Cheng ◽  
Can Shao
Keyword(s):  
Response Time ◽  
Change Point ◽  
False Positive Rate ◽  
Critical Values ◽  
Response Time Data ◽  
Change Point Analysis ◽  
Ratio Test ◽  
Time Data ◽  
Test Speededness ◽  
Point Analysis

Computer-based and web-based testing have become increasingly popular in recent years. Their popularity has dramatically expanded the availability of response time data. Compared to the conventional item response data that are often dichotomous or polytomous, response time has the advantage of being continuous and can be collected in an unobstrusive manner. It therefore has great potential to improve many measurement activities. In this paper, we propose a change point analysis (CPA) procedure to detect test speededness using response time data. Specifically, two test statistics based on CPA, the likelihood ratio test and Wald test, are proposed to detect test speededness. A simulation study has been conducted to evaluate the performance of the proposed CPA procedure, as well as the use of asymptotic and empirical critical values. Results indicate that the proposed procedure leads to high power in detecting test speededness, while keeping the false positive rate under control, even when simplistic and liberal critical values are used. Accuracy of the estimation of the actual change point, however, is highly dependent on the true change point. A real data example is also provided to illustrate the utility of the proposed procedure and its contrast to the response-only procedure. Implications of the findings are discussed at the end.

A Sequential Bayesian Change-Point Analysis of BRICS Currency Returns

2021 ◽  
Author(s):  
Aviral Kumar Tiwari ◽  
Cleiton Guollo Taufemback ◽  
Satish Kumar
Keyword(s):  
Change Point ◽  
Change Point Analysis ◽  
Point Analysis

Detection of Test Speededness Using Change-Point Analysis

Psychometrika ◽  
2015 ◽  
Vol 81 (4) ◽  
pp. 1118-1141 ◽  
Author(s):  
Can Shao ◽  
Jun Li ◽  
Ying Cheng
Keyword(s):  
Change Point ◽  
Change Point Analysis ◽  
Test Speededness ◽  
Point Analysis
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