ClinicalPath: A Visualization Tool to Improve the Evaluation of Electronic Health Records in Clinical Decision-Making

Linhares, Cláudio D. G.; Lima, Daniel Magalhães; Ponciano, Jean R.; Olivatto, Mauro M; Gutierrez, Marco Antônio; Poco, Jorge; Traina, Caetano; Traina, Agma J. M.

doi:10.1109/tvcg.2022.3175626

Cited by 12 publications

(2 citation statements)

References 34 publications

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“…For instance, LifeLines [PMR * 96] visualizes biographies of persons, separating events into several rows. Similarly, Clinical‐Path [LLP * 22] uses several rows to displays an overview of patient's test results. Bombalytics [AWB20] shows matches in a bomb laying game; players and game objects form the rows of a timeline, connected if a player interacts with an object.…”

Section: Related Workmentioning

confidence: 99%

VisCoMET: Visually Analyzing Team Collaboration in Medical Emergency Trainings

Liebers

Agarwal

Krug

et al. 2023

Computer Graphics Forum

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Section: Related Workmentioning

confidence: 99%

VisCoMET: Visually Analyzing Team Collaboration in Medical Emergency Trainings

Liebers

Agarwal

Krug

et al. 2023

Computer Graphics Forum

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“…Given these layouts' strengths and characteristics, they have recently been adapted to enhance analyses and decision making in health contexts. Applications include the simulation and analysis of infection spreads ] and the inspection of patients' clinical exams results Linhares et al 2022].…”

Section: Related Workmentioning

confidence: 99%

A Visualization Approach for Simulating and Analyzing Infection Spread Dynamics Using Temporal Networks

Ponciano

Vezono

Linhares

2022

jidm

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Temporal networks have been widely used to model instances of a domain of interest and their time-evolving interaction, including modeling individuals and face-to-face contacts throughout time. In the context of infection spread, such individuals can, e.g., remain susceptible, recovered, or be infected at a particular time. Understanding the infection spread behavior (its speed and magnitude, for instance) is crucial for quick and reliable decision making. Network visualization strategies can help in this task as they allow easy identification of who infected whom and when, epidemics outbreak, and other relevant aspects. This paper presents a visualization approach for the simulation and analysis of infection spread dynamics that considers different infection probabilities and different levels of social distancing (inter-group interaction). We performed quantitative and visual experiments using three real-world social networks with distinct characteristics and from two different environments. Our findings reveal the overall influence of different levels of inter-group interaction and infection probabilities in the infection spread dynamics and also demonstrate the usefulness of our approach for enhanced local (individual- or group-level) investigations.

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