MyHealthAvatar (MHA) is built on the latest information and communications technology with the aim of collecting lifestyle and health data to promote citizen’s wellbeing. According to the collected data, MHA offers visual analytics of lifestyle data, contributes to individualised disease prediction and prevention, and supports healthy lifestyles and independent living. The iManageCancer project aims to empower patients and strengthen self-management in cancer diseases. Therefore, MHA has contributed to the iManageCancer scenario and provides functionality to the iManageCancer platform in terms of its support of lifestyle management of cancer patients by providing them with services to help their cancer management. This paper presents two different MHA-based Android applications for breast and prostate cancer patients. The components in these apps facilitate health and lifestyle data presentation and analysis, including weight control, activity, mood and sleep data collection, promotion of physical exercise after surgery, questionnaires and helpful information. These components can be used cooperatively to achieve flexible visual analysis of spatiotemporal lifestyle and health data and can also help patients discover information about their disease and its management.
Scientific researchers are facing a rapidly growing volume of literatures nowadays. While these publications offer rich and valuable information, the scale of the datasets makes it difficult for the researchers to manage and search for desired information efficiently. Literature Explorer is a new interactive visual analytics suite that facilitates the access to desired scientific literatures through mining and interactive visualisation. We propose a novel topic mining method that is able to uncover "thematic topics" from a scientific corpus. These thematic topics have an explicit semantic association to the research themes that are commonly used by human researchers in scientific fields, and hence are human interpretable. They also contribute to effective document retrieval. The visual analytics suite consists of a set of visual components that are closely coupled with the underlying thematic topic detection to support interactive document retrieval. The visual components are adequately integrated under the design rationale and goals. Evaluation results are given in both objective measurements and subjective terms through expert assessments. Comparisons are also made against the outcomes from the traditional topic modelling methods.
Abstract. With the rise of wearable sensor technologies, an increasing number of wearable health and medical sensors are available on the market, which enables not only people but also doctors to utilise them to monitor people's health in such a consistent way that the sensors may become people's lifetime companion. The consistent measurements from a variety of wearable sensors implies that a huge amount of data needs to be processed, which cannot be achieved by traditional processing methods. Visual analytics is designed to promote knowledge discovery and utilisation of big data via mature visual paradigms with well-designed user interactions and has become indispensable in big data analysis. In this paper we introduce the role of visual analytics for health monitoring and risk management in the European Commission funded project CARRE which aims to provide innovative means for the management of cardiorenal diseases with the assistance of wearable sensors. The visual analytics components of timeline and parallel coordinates for health monitoring and of node-link diagrams, chord diagrams and sankey diagrams for risk analysis are presented to achieve ubiquitous and lifelong health and risk monitoring to promote people's health.
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