DecisionFlow: Visual Analytics for High-Dimensional Temporal Event Sequence Data

Gotz, David; Stavropoulos, Harry

doi:10.1109/tvcg.2014.2346682

Cited by 160 publications

(125 citation statements)

References 26 publications

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“…Given our problem, we discuss the second category. These visualizations focus on visualizing data from a group of patients in order to assist clinical researchers and quality assurance analysts in assessing and improving the quality of care of patients [13,15,19,33,36].…”

Section: Related Workmentioning

confidence: 99%

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TimeSpan: Using Visualization to Explore Temporal Multi-dimensional Data of Stroke Patients

Loorak

Périn

Kamal

et al. 2016

IEEE Trans. Visual. Comput. Graphics

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Abstract-We present TimeSpan, an exploratory visualization tool designed to gain a better understanding of the temporal aspects of the stroke treatment process. Working with stroke experts, we seek to provide a tool to help improve outcomes for stroke victims. Time is of critical importance in the treatment of acute ischemic stroke patients. Every minute that the artery stays blocked, an estimated 1.9 million neurons and 12 km of myelinated axons are destroyed. Consequently, there is a critical need for efficiency of stroke treatment processes. Optimizing time to treatment requires a deep understanding of interval times. Stroke health care professionals must analyze the impact of procedures, events, and patient attributes on time-ultimately, to save lives and improve quality of life after stroke. First, we interviewed eight domain experts, and closely collaborated with two of them to inform the design of TimeSpan. We classify the analytical tasks which a visualization tool should support and extract design goals from the interviews and field observations. Based on these tasks and the understanding gained from the collaboration, we designed TimeSpan, a web-based tool for exploring multi-dimensional and temporal stroke data. We describe how TimeSpan incorporates factors from stacked bar graphs, line charts, histograms, and a matrix visualization to create an interactive hybrid view of temporal data. From feedback collected from domain experts in a focus group session, we reflect on the lessons we learned from abstracting the tasks and iteratively designing TimeSpan. Permanent repository link

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Section: Related Workmentioning

confidence: 99%

“…OutFlow [35] and its successor DecisionFlow [13] adopt Sankey Diagrams [24] to analyse disease progress and outcomes in patient records. DecisionFlow extends OutFlow by using aggregation techniques for the visual encoding of high-dimensional temporal event sequence data.…”

Section: Related Workmentioning

confidence: 99%

TimeSpan: Using Visualization to Explore Temporal Multi-dimensional Data of Stroke Patients

Loorak

Périn

Kamal

et al. 2016

IEEE Trans. Visual. Comput. Graphics

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“…It also claims to be backed by a Temporal Query Server optimized to support complex temporal queries on large databases. Another system for constructing visual temporal queries is DecisionFlow [37]. DecisionFlow enables interactive queries on high-dimensional datasets (i.e.…”

Section: State-of-the-artmentioning

confidence: 99%

Visualisation of Integrated Patient-Centric Data as Pathways: Enhancing Electronic Medical Records in Clinical Practice

Bettencourt‐Silva

Mannu

Iglesia

2016

Lecture Notes in Computer Science

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Abstract. Routinely collected data in hospital Electronic Medical Records (EMR) is rich and abundant but often not linked or analysed for purposes other than direct patient care. We have created a methodology to integrate patient-centric data from different EMR systems into clinical pathways that represent the history of all patient interactions with the hospital during the course of a disease and beyond. In this paper, the literature in the area of data visualisation in healthcare is reviewed and a method for visualising the journeys that patients take through care is discussed. Examples of the hidden knowledge that could be discovered using this approach are explored and the main application areas of visualisation tools are identified. This paper also highlights the challenges of collecting and analysing such data and making the visualisations extensively used in the medical domain. This paper starts by presenting the state-of-the-art in visualisation of clinical and other health related data. Then, it describes an example clinical problem and discusses the visualisation tools and techniques created for the utilisation of these data by clinicians and researchers. Finally, we look at the open problems in this area of research and discuss future challenges.

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“…They extract and highlight the common event sequences from patient records. Outflow (Wongsuphasawat and Gotz 2012) and DecisionFlow (Gotz and Stavropoulos 2014) use Sankey diagram (Riehmann et al 2005) style visualisation to help visualise and analyse the causal relationships of events in complex event sequences.…”

Section: Related Workmentioning

confidence: 99%

Integrated visualisation of wearable sensor data and risk models for individualised health monitoring and risk assessment to promote patient empowerment

Zhao

Parvinzamir

Wilson

et al. 2016

J Vis

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Patient empowerment delivers health and social care services that enable people to gain more control of their healthcare needs. With the advancement of sensor technologies, it is increasingly possible to monitor people's health with dedicated wearable sensors. The consistent measurements from a variety of wearable sensors imply that a huge amount of data may be exploited to monitor and predict people's health using medically proven models. In the process of health data representation and analysis, visualisation can be employed to promote data analysis and knowledge discovery via mature visual paradigms and welldesigned user interactions. In this paper, we introduce the role of visualisation for individualised health monitoring and risk management in the background of a European Commission funded project, which aims to provide self-management of cardiorenal diseases with the assistance of wearable sensors. The DOI 10.1007/s12650-016-0402-6 visualisation components of health monitoring, risk model exploration, and risk analysis are presented to achieve personalised health and risk monitoring and to promote people's wellbeing. It allows the patients not only to view existing risks, but also to gain awareness of the right pathway to change their lifestyles in order to reduce potential health risks.

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DecisionFlow: Visual Analytics for High-Dimensional Temporal Event Sequence Data

Cited by 160 publications

References 26 publications

TimeSpan: Using Visualization to Explore Temporal Multi-dimensional Data of Stroke Patients

TimeSpan: Using Visualization to Explore Temporal Multi-dimensional Data of Stroke Patients

Visualisation of Integrated Patient-Centric Data as Pathways: Enhancing Electronic Medical Records in Clinical Practice

Integrated visualisation of wearable sensor data and risk models for individualised health monitoring and risk assessment to promote patient empowerment

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