Visualisation Methods of Hierarchical Biological Data: A Survey and Review

The sheer amount of high dimensional biomedical data requires machine learning, and advanced data visualization techniques to make the data understandable for human experts. Most biomedical data today is in arbitrary high dimensional spaces, and is not directly accessible to the human expert for a visual and interactive analysis process. To cope with this challenge, the application of machine learning and knowledge extraction methods is indispensable throughout the entire data analysis workflow. Nevertheless, human experts need to understand and interpret the data and experimental results. Appropriate understanding is typically supported by visualizing the results adequately, which is not a simple task. Consequently, data visualization is one of the most crucial steps in conveying biomedical results. It can and should be considered as a critical part of the analysis pipeline. Still as of today, 2D representations dominate, and human perception is limited to this lower dimension to understand the data. This makes the visualization of the results in an understandable and comprehensive manner a grand challenge. This paper reviews the current state of visualization methods in a biomedical context. It focuses on hierarchical biological data as a source for visualization, and gives a comprehensiv

HCI for health and wellbeing: challenges and opportunities

In terms of Human–Computer Interaction, healthcare presents paradoxes: on the one hand, there is substantial investment in innovative health technologies, particularly around “big data” analytics and personal health technologies; on the other hand, most interactive health technologies that are currently deployed at scale are difficult to use and few innovative technologies have achieved significant market penetration. We live in a time of change, with a shift from care being delivered by professionals towards people being expected to be actively engaged and involved in shared decision making. Technically, this shift is supported by novel health technologies and information resources; culturally, the pace of change varies across contexts. In this paper, I present a “space” of interactive health technologies, users and uses, and interdependencies between them. Based on a review of the past and present, I highlight opportunities for and challenges to the application of HCI methods in the design and deployment of digital health technologies. These include threats to privacy, patient trust and experience, and opportunities to deliver healthcare and empower people to manage their health and wellbeing in ways that better fit their lives and values

Visual Analytics for Performing Complex Tasks with Electronic Health Records

Electronic health record systems (EHRs) facilitate the storage, retrieval, and sharing of patient health data; however, the availability of data does not directly translate to support for tasks that healthcare providers encounter every day. In recent years, healthcare providers employ a large volume of clinical data stored in EHRs to perform various complex data-intensive tasks. The overwhelming volume of clinical data stored in EHRs and a lack of support for the execution of EHR-driven tasks are, but a few problems healthcare providers face while working with EHR-based systems. Thus, there is a demand for computational systems that can facilitate the performance of complex tasks that involve the use and working with the vast amount of data stored in EHRs. Visual analytics (VA) offers great promise in handling such information overload challenges by integrating advanced analytics techniques with interactive visualizations. The user-controlled environment that VA systems provide allows healthcare providers to guide the analytics techniques on analyzing and managing EHR data through interactive visualizations. The goal of this research is to demonstrate how VA systems can be designed systematically to support the performance of complex EHR-driven tasks. In light of this, we present an activity and task analysis framework to analyze EHR-driven tasks in the context of interactive visualization systems. We also conduct a systematic literature review of EHR-based VA systems and identify the primary dimensions of the VA design space to evaluate these systems and identify the gaps. Two novel EHR-based VA systems (SUNRISE and VERONICA) are then designed to bridge the gaps. SUNRISE incorporates frequent itemset mining, extreme gradient boosting, and interactive visualizations to allow users to interactively explore the relationships between laboratory test results and a disease outcome. The other proposed system, VERONICA, uses a representative set of supervised machine learning techniques to find the group of features with the strongest predictive power and make the analytic results accessible through an interactive visual interface. We demonstrate the usefulness of these systems through a usage scenario with acute kidney injury using large provincial healthcare databases from Ontario, Canada, stored at ICES

Making Sense of Online Public Health Debates with Visual Analytics Systems

Online debates occur frequently and on a wide variety of topics. Particularly, online debates about various public health topics (e.g., vaccines, statins, cannabis, dieting plans) are prevalent in today’s society. These debates are important because of the real-world implications they can have on public health. Therefore, it is important for public health stakeholders (i.e., those with a vested interest in public health) and the general public to have the ability to make sense of these debates quickly and effectively. This dissertation investigates ways of enabling sense-making of these debates with the use of visual analytics systems (VASes). VASes are computational tools that integrate data analytics (e.g., webometrics or natural language processing), data visualization, and human-data interaction. This dissertation consists of three stages. In the first stage, I describe the design and development of a novel VAS, called VINCENT (VIsual aNalytiCs systEm for investigating the online vacciNe debaTe), for making sense of the online vaccine debate. VINCENT helps users to make sense of data (i.e., online presence, geographic location, sentiments, and focus) from a collection of vaccine focused websites. In the second stage, I discuss the results of a user study of VINCENT. Participants in the study were asked to complete a set of ten sense-making tasks that required investigating a provided set of websites. Based on the positive outcomes of the study, in stage three of the dissertation I generalize the findings from the first two stages and present a framework called ODIN (Online Debate entIty aNalyzer). This framework consists of various attributes that are important to consider when analyzing online public health debates and provides methods of collecting and analyzing that data. Overall, this dissertation provides visual analytics researchers an in-depth analysis on the considerations and challenges for creating VASes to make sense of online public health debates

Visual analytics for concept exploration in subspaces of patient groups : Making sense of complex datasets with the Doctor-in-the-loop

Medical doctors and researchers in bio-medicine are increasingly confronted with complex patient data, posing new and difficult analysis challenges. These data are often comprising high-dimensional descriptions of patient conditions and measurements on the success of certain therapies. An important analysis question in such data is to compare and correlate patient conditions and therapy results along with combinations of dimensions. As the number of dimensions is often very large, one needs to map them to a smaller number of relevant dimensions to be more amenable for expert analysis. This is because irrelevant, redundant, and conflicting dimensions can negatively affect effectiveness and efficiency of the analytic process (the so-called curse of dimensionality). However, the possible mappings from high- to low-dimensional spaces are ambiguous. For example, the similarity between patients may change by considering different combinations of relevant dimensions (subspaces). We demonstrate the potential of subspace analysis for the interpretation of high-dimensional medical data. Specifically, we present SubVIS, an interactive tool to visually explore subspace clusters from different perspectives, introduce a novel analysis workflow, and discuss future directions for high-dimensional (medical) data analysis and its visual exploration. We apply the presented workflow to a real-world dataset from the medical domain and show its usefulness with a domain expert evaluation.publishe