Intelligent visualization and exploration of time-oriented data of multiple patients

Klimov, Denis; Shaḥar, Yuval; Taieb-Maimon, Meirav

doi:10.1016/j.artmed.2010.02.001

Cited by 95 publications

(58 citation statements)

References 36 publications

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“…These visualizations of abstracted data (cp. [16,19]) can be integrated directly in our design and allow continuous exploration from overview to detail as we demonstrate with the semantic zoom chart. Furthermore, this should allow users to jointly explore variables of significantly different sampling frequency.…”

Section: Discussionmentioning

confidence: 99%

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Visual Exploration of Time-Oriented Patient Data for Chronic Diseases: Design Study and Evaluation

Rind

Aigner

Miksch

et al. 2011

Lecture Notes in Computer Science

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Abstract. Medical care, particularly for chronic diseases, accumulates a huge amount of patient data over extensive time periods that needs to be accessed and analyzed accordingly. Information Visualization methods hold great promises in turning data deluge into improved quality of medical care. Yet, patient data management systems mostly provide documents, form-based displays, or static visualizations. We present a design study of an interactive visualization system, called VisuExplore, to support long-term care and medical analysis of patients with chronic diseases. VisuExplore offers interaction techniques for effective exploration of time-oriented data and employs simple, but intuitive visualization techniques. It was developed in close cooperation with physicians. We conducted two user studies with nine physicians and 16 students, which indicate that our design is useful and appropriate for particular tasks.

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Section: Discussionmentioning

confidence: 99%

“…However, these designs cater more to different tasks than open-ended exploration. In the surroundings of this work we also find the problem domains of querying patient databases (e.g., LifeLines2 [34], Similan [35]) and exploring patient cohorts (e.g., CareGiver [5], Dare [7], Gravi++ [14], TimeRider [27], VISITORS [16]). …”

Section: Related Workmentioning

confidence: 99%

Visual Exploration of Time-Oriented Patient Data for Chronic Diseases: Design Study and Evaluation

Rind

Aigner

Miksch

et al. 2011

Lecture Notes in Computer Science

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“…The work does not provide, however, capabilities for aggregation of patients according to some dynamic criteria. In further work [28], the authors provided such capability under a system called VISITORS.…”

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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“…VISITORS (Klimov et al 2010), which is based on KNAVE (Shahar and Cheng 1999) and KNAVE II (Shahar et al 2006), uses aggregation to extract meaningful interpretations from multiple patients' raw timeoriented data. PatternFinder (Fails et al 2006) provides tools for the user to query patterns by specifying the attributes of events and time spans.…”

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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Intelligent visualization and exploration of time-oriented data of multiple patients

Cited by 95 publications

References 36 publications

Visual Exploration of Time-Oriented Patient Data for Chronic Diseases: Design Study and Evaluation

Visual Exploration of Time-Oriented Patient Data for Chronic Diseases: Design Study and Evaluation

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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