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 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 A data-driven missing value imputation approach for longitudinal datasets

2021 ◽  
Author(s):  
Caio Ribeiro ◽  
Alex A. Freitas
Keyword(s):  
Missing Data ◽  
Longitudinal Data ◽  
Missing Values ◽  
Error Rates ◽  
Imputation Method ◽  
Data Driven ◽  
Missing Value ◽  
Missing Value Imputation ◽  
Human Ageing ◽  
Imputation Approach

AbstractLongitudinal datasets of human ageing studies usually have a high volume of missing data, and one way to handle missing values in a dataset is to replace them with estimations. However, there are many methods to estimate missing values, and no single method is the best for all datasets. In this article, we propose a data-driven missing value imputation approach that performs a feature-wise selection of the best imputation method, using known information in the dataset to rank the five methods we selected, based on their estimation error rates. We evaluated the proposed approach in two sets of experiments: a classifier-independent scenario, where we compared the applicabilities and error rates of each imputation method; and a classifier-dependent scenario, where we compared the predictive accuracy of Random Forest classifiers generated with datasets prepared using each imputation method and a baseline approach of doing no imputation (letting the classification algorithm handle the missing values internally). Based on our results from both sets of experiments, we concluded that the proposed data-driven missing value imputation approach generally resulted in models with more accurate estimations for missing data and better performing classifiers, in longitudinal datasets of human ageing. We also observed that imputation methods devised specifically for longitudinal data had very accurate estimations. This reinforces the idea that using the temporal information intrinsic to longitudinal data is a worthwhile endeavour for machine learning applications, and that can be achieved through the proposed data-driven approach.

scholarly journals Active Electronically Steered Array (AESA) Radar Data Visualization Software

2019 ◽  
Vol 16 (1) ◽  
pp. 107-114
Author(s):  
K. Kavitha ◽  
◽  
E. Srinivas Reddy ◽  
Dr. N.V Rao ◽  
◽  
...  
Keyword(s):  
Data Visualization ◽  
Radar Data ◽  
Visualization Software

System for Detecting Driver Alertness while Driving

2021 ◽  
Vol 9 (VI) ◽  
pp. 2095-2099
Author(s):  
Prajwal Chandrakant Sapkal
Keyword(s):  
Aspect Ratio ◽  
Real Time ◽  
Raspberry Pi ◽  
Time Data ◽  
Facial Landmarks ◽  
Remote Location ◽  
Python Language ◽  
Falling Asleep ◽  
Time Basis ◽  
Main Component

In this project, we are going to present a system for sleep detection alarm to monitor the driver, based on the real time surveillance and alert him as well as post it at remote location whenever it’s necessary using cloud platform. This device is to be developed using the Raspberry Pi, Open CV library and camera module. The required coding part of the project will be done using Python language. The main component of the project will be pretrained landmark detector as a software part. It identifies 68 points on the human face. The Dlib’s landmark will detect 68 facial landmarks which enables us to extract the various facial structures using simple Python array slices. The facial landmarks of fully closed eye and a fully opened eye will be first plotted. This data is further processed and tested with some results which will give the information about driver’s alertness. Once the facial landmarks associated with an eye are determined, we can apply the Eye Aspect Ratio (EAR) algorithm. In our case, we’ll be monitoring the eye aspect ratio to see if the values of the facial landmarks, thus implying that the driver/user has closed their eyes or distracted from driving or yawn. Once implemented, our algorithm will start by localising the facial landmarks on real time basis. We can then will be able to monitor the eye aspect ratio to determine if the eyes are close or nearly close which will be the indicator for driver is falling asleep. And then finally raising an alarm if the eye aspect ratio is below a pre-defined threshold for a sufficiently long amount of time. The alarm will be loud enough to wake up the driver and bring back his attention. At the same time data is passed to remote location using cloud whenever it’s necessary.

scholarly journals EM algorithm for longitudinal data with non-ignorable missing values: An application to health data

2016 ◽  
Vol 27 (2) ◽  
pp. 133-142
Author(s):  
Radia Taisir ◽  
M Ataharul Islam
Keyword(s):  
Longitudinal Data ◽  
Em Algorithm ◽  
Expectation Maximization ◽  
Missing Values ◽  
Likelihood Method ◽  
Estimation Methods ◽  
Binary Response ◽  
Complex Method ◽  
Missing Responses ◽  
Regression Parameters

Longitudinal studies involves repeated observations over time on the same experimental units and missingness may occur in non-ignorable fashion. For such longitudinal missing data, a Markov model may be used to model the binary response along with a suitable non-response model for the missing portion of the data. It is of the primary interest to estimate the effects of covariates on the binary response. Similar model for such incomplete longitudinal data exists where estimation of the regression parameters are obtained using likelihood method by summing over all possible values of the missing responses. In this paper, we propose an expectation-maximization (EM) algorithm technique for the estimation of the regression parameters which is computationally simple and produces similar efficient estimates as compared to the existing complex method of estimation. A comparison of the existing and the proposed estimation methods has been made by analyzing the Health and Retirement Survey (HRS) data of United States.Bangladesh J. Sci. Res. 27(2): 133-142, December-2014

Augmented Reality Based Real-Time Data Visualization Concept for Ultra-Efficient Car Prototype

2017 ◽  
Author(s):  
Peter Wozniak ◽  
Oliver Vauderwange ◽  
Nicolas Javahiraly ◽  
Dan Curticapean
Keyword(s):  
Augmented Reality ◽  
Real Time ◽  
Data Visualization ◽  
Time Data ◽  
Real Time Data
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