Towards a Structural Framework for Explicit Domain Knowledge in Visual Analytics

Clinicians and other analysts working with healthcare data are in need for better support to cope with large and complex data. While an increasing number of visual analytics environments integrates explicit domain knowledge as a means to deliver a precise representation of the available data, theoretical work so far has focused on the role of knowledge in the visual analytics process. There has been little discussion about how such explicit domain knowledge can be structured in a generalized framework. This paper collects desiderata for such a structural framework, proposes how to address these desiderata based on the model of linked data, and demonstrates the applicability in a visual analytics environment for physiotherapy.Comment: 8 pages, 5 figure

On the cultivation of proper abstraction

This paper discusses the basis, order, and motives for creating correct abstract representations in software engineering. The problem of using poor quality (ill-conceived, undefined, harmful) abstractions significantly affects the life cycle of software, narrows the range of thoughtful solutions, and reduces the reliability of a software product. To improve the quality of abstraction, a number of possible directions for finding and implementing abstract representations have been considered. Relying on a thorough literature analysis as well as on the author's own introspective experience, a strategy for finding the correct abstraction through the coherence of a concrete compact formulation and its abstract expression is proposed. Within the framework of the proposed strategy, two fundamental principles of producing a correct abstraction are found: integrity and purposefulness. The first will allow one to see the whole picture without omitting details. Purposefulness will resolve the alignment of the abstraction with both the means to the end and the end itself. Strategic coherence is provided by individual creativity, self-assessment, motivation, and accountability for the result

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 of Electronic Health Records with a focus on Acute Kidney Injury

The increasing use of electronic platforms in healthcare has resulted in the generation of unprecedented amounts of data in recent years. The amount of data available to clinical researchers, physicians, and healthcare administrators continues to grow, which creates an untapped resource with the ability to improve the healthcare system drastically. Despite the enthusiasm for adopting electronic health records (EHRs), some recent studies have shown that EHR-based systems hardly improve the ability of healthcare providers to make better decisions. One reason for this inefficacy is that these systems do not allow for human-data interaction in a manner that fits and supports the needs of healthcare providers. Another reason is the information overload, which makes healthcare providers often misunderstand, misinterpret, ignore, or overlook vital data. The emergence of a type of computational system known as visual analytics (VA), has the potential to reduce the complexity of EHR data by combining advanced analytics techniques with interactive visualizations to analyze, synthesize, and facilitate high-level activities while allowing users to get more involved in a discourse with the data. The purpose of this research is to demonstrate the use of sophisticated visual analytics systems to solve various EHR-related research problems. This dissertation includes a framework by which we identify gaps in existing EHR-based systems and conceptualize the data-driven activities and tasks of our proposed systems. Two novel VA systems (VISA_M3R3 and VALENCIA) and two studies are designed to bridge the gaps. VISA_M3R3 incorporates multiple regression, frequent itemset mining, and interactive visualization to assist users in the identification of nephrotoxic medications. Another proposed system, VALENCIA, brings a wide range of dimension reduction and cluster analysis techniques to analyze high-dimensional EHRs, integrate them seamlessly, and make them accessible through interactive visualizations. The studies are conducted to develop prediction models to classify patients who are at risk of developing acute kidney injury (AKI) and identify AKI-associated medication and medication combinations using EHRs. Through healthcare administrative datasets stored at the ICES-KDT (Kidney Dialysis and Transplantation program), London, Ontario, we have demonstrated how our proposed systems and prediction models can be used to solve real-world problems