Visualization for Biological Models, Simulation, and Ontologies

In this dissertation, I present three browsers that I have developed for the purpose of exploring, understanding, and analyzing models, simulations, and ontologies in biology and medicine. The first browser visualizes multidimensional simulation data as an animation. The second browser visualizes the equations of a complex model as a network and puts structure and organization on top of equations and variables. The third browser is an ontology viewer and editor, directly intended for the Foundational Model of Anatomy (FMA), but applicable to other ontologies as well. This browser has two contributions. First, it is a lightweight deliverable that lets someone easily dabble with the FMA. Second, it lets the user edit an ontology to create a view of it. For the ontology browser, I also conduct user studies to refine and evaluate the software

Doctor of Philosophy

dissertationA broad range of applications capture dynamic data at an unprecedented scale. Independent of the application area, finding intuitive ways to understand the dynamic aspects of these increasingly large data sets remains an interesting and, to some extent, unsolved research problem. Generically, dynamic data sets can be described by some, often hierarchical, notion of feature of interest that exists at each moment in time, and those features evolve across time. Consequently, exploring the evolution of these features is considered to be one natural way of studying these data sets. Usually, this process entails the ability to: 1) define and extract features from each time step in the data set; 2) find their correspondences over time; and 3) analyze their evolution across time. However, due to the large data sizes, visualizing the evolution of features in a comprehensible manner and performing interactive changes are challenging. Furthermore, feature evolution details are often unmanageably large and complex, making it difficult to identify the temporal trends in the underlying data. Additionally, many existing approaches develop these components in a specialized and standalone manner, thus failing to address the general task of understanding feature evolution across time. This dissertation demonstrates that interactive exploration of feature evolution can be achieved in a non-domain-specific manner so that it can be applied across a wide variety of application domains. In particular, a novel generic visualization and analysis environment that couples a multiresolution unified spatiotemporal representation of features with progressive layout and visualization strategies for studying the feature evolution across time is introduced. This flexible framework enables on-the-fly changes to feature definitions, their correspondences, and other arbitrary attributes while providing an interactive view of the resulting feature evolution details. Furthermore, to reduce the visual complexity within the feature evolution details, several subselection-based and localized, per-feature parameter value-based strategies are also enabled. The utility and generality of this framework is demonstrated by using several large-scale dynamic data sets

Insights from mechanistic and digital intervention approaches

Eine effektive Förderung des Sozialverhaltens autistischer Kinder erfordert ein tiefgehendes Verständnis der Ursachen maladaptiver Reaktionen und die Bereitstellung eines leicht verfügbaren Förderangebots. Hier haben digitale Angebote ein großes Potential. Ebenso wie bei der Ursachenforschung mangelt es jedoch an Forschungsansätzen, welche eine Vielzahl an kognitiven und emotionalen Prozessen in die digitale Förderung integrieren. Dementsprechend untersuchte die Dissertation zunächst das Zusammenspiel verschiedener Ursachen aggressives Sozialverhaltens anhand eines etablierten Modells der sozial-kognitiven Informationsverarbeitung. Durch die Integration verschiedener Facetten der Empathie und deren zugrundeliegende Kompetenzen in ein digitales Förderangebot sollte im zweiten Schritt eine Verbesserung des Sozialverhaltens autistischer Grundschüler:innen erreicht werden. Es zeigte sich, dass Emotionsdysregulation verschiedene Formen aggressiven Sozialverhaltens und damit assoziierte feindselige Attributionen verstärkt. Letztere stand vor allem mit verbalen und verdeckten Aggressionsformen sowie mit guten Emotionserkennungsfertigkeiten im Zusammenhang. Eine Verbesserung des Sozialverhaltens und der Emotionsregulation konnte mittelfristig durch das sechswöchige, eltern-begleitete eLearningprogramm „Zirkus Empathico“ erreicht werden. Die multizentrische, randomisiert kontrollierte Studie ergab zudem kurzfristige und moderate Interventionseffekte für Empathie und Emotionserkennung als primäre Endpunkte. Insgesamt unterstreicht die Dissertation die Validität etablierter Modelle der sozialen Informationsverarbeitung sowie die Relevanz, diese zukünftigen Forschungs- und Interventionsansätzen zugrunde zu legen. Durch die Integration verschiedener sozio-emotionaler Kompetenzen scheint die digitale Intervention Zirkus Empathico prosoziales Verhalten autistischer Kinder auf effektive und praktikable Weise zu fördern.Effective training of autistic children`s social behavior requires an in-depth understanding of the causes of maladaptive responses and the provision of easily accessible support services. In this context, digital interventions have great potential. However, there is a lack of research approaches that integrate a variety of cognitive and emotional processes into both, explanation and digital support. The present dissertation first examined the interplay of different causes of aggressive social behavior by applying an established model of social-cognitive information processing to a sample of autistic elementary school students. Second, by integrating different facets of empathy and their underlying competencies into a digital program, the social behavior of autistic elementary school children should be improved. First, it was shown that emotion dysregulation strengthens various forms of aggressive social behavior and associated hostile attribution biases. The latter was mainly related to verbal and covert forms of aggression and good emotion recognition skills. Second, the parent-assisted eLearning program "Zirkus Empathico" led to a medium-term improvement in social behavior and emotion regulation after a six-week training. In addition, the multicenter randomized controlled trial showed moderate intervention effects on empathy and emotion recognition as primary outcomes, which were no longer detectable three months later. Overall, the dissertation highlights the validity of established models of social information processing and the relevance of using them as a foundation for future research and intervention. By integrating various socio-emotional competencies, the digital intervention Zirkus Empathico seems to strengthen autistic children’s prosocial behavior effectively and feasibly

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

Cognitive Foundations for Visual Analytics

In this report, we provide an overview of scientific/technical literature on information visualization and VA. Topics discussed include an update and overview of the extensive literature search conducted for this study, the nature and purpose of the field, major research thrusts, and scientific foundations. We review methodologies for evaluating and measuring the impact of VA technologies as well as taxonomies that have been proposed for various purposes to support the VA community. A cognitive science perspective underlies each of these discussions

EHR STAR: The State‐Of‐the‐Art in Interactive EHR Visualization

Since the inception of electronic health records (EHR) and population health records (PopHR), the volume of archived digital health records is growing rapidly. Large volumes of heterogeneous health records require advanced visualization and visual analytics systems to uncover valuable insight buried in complex databases. As a vibrant sub-field of information visualization and visual analytics, many interactive EHR and PopHR visualization (EHR Vis) systems have been proposed, developed, and evaluated by clinicians to support effective clinical analysis and decision making. We present the state-of-the-art (STAR) of EHR Vis literature and open access healthcare data sources and provide an up-to-date overview on this important topic. We identify trends and challenges in the field, introduce novel literature and data classifications, and incorporate a popular medical terminology standard called the Unified Medical Language System (UMLS). We provide a curated list of electronic and population healthcare data sources and open access datasets as a resource for potential researchers, in order to address one of the main challenges in this field. We classify the literature based on multidisciplinary research themes stemming from reoccurring topics. The survey provides a valuable overview of EHR Vis revealing both mature areas and potential future multidisciplinary research directions

Three--dimensional medical imaging: Algorithms and computer systems

This paper presents an introduction to the field of three-dimensional medical imaging It presents medical imaging terms and concepts, summarizes the basic operations performed in three-dimensional medical imaging, and describes sample algorithms for accomplishing these operations. The paper contains a synopsis of the architectures and algorithms used in eight machines to render three-dimensional medical images, with particular emphasis paid to their distinctive contributions. It compares the performance of the machines along several dimensions, including image resolution, elapsed time to form an image, imaging algorithms used in the machine, and the degree of parallelism used in the architecture. The paper concludes with general trends for future developments in this field and references on three-dimensional medical imaging

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

A Visual Approach to Improving the Experience of Health Information for Vulnerable Individuals

Many individuals with low health literacy (LHL) and limited English proficiency (LEP) have poor experiences consuming health information: they find it unengaging, unappealing, difficult to understand, and un-motivating. These negative experiences may blunt, or even sabotage, the desired effect of communicating health information: to increase engagement and ability to manage health. It is imperative to find solutions to improve poor experiences of health information, because such experiences heighten vulnerability to poor health outcomes. We aimed to address a gap in the health literacy literature by studying the patient experience of health information and how visualization might be able to help. Our four studies involved patients presented with health information in various settings to improve understanding and management of their care. We used semi-structured interviews and observations to understand patient experiences of receiving personal health information in the hospital. We learned that the return of results is desired and has the potential to promote patient engagement with care. We developed a novel method to analyze LHL, LEP caregiver experience and information needs in the community setting. The novel method increased our understanding and ability to detect differences in experiences within the same ethnic group, based on language preference. Next, we interrogated the literature for a solution to easily communicate complicated health information to disinterested, LHL, LEP individuals. We found that visualizations can help increase interest, comprehension, support faster communication, and even help broach difficult topics. Finally, our findings were used to develop a novel prototype to improve experiences of consuming genetic risk information for those having LHL and LEP. Unlike traditional approaches that focus on communicating risk numbers and probabilities, the novelty of our approach was that we focused on communicating risk as a feeling. We achieved this by leveraging vicarious learning via real patient experience materials (e.g., quotes, videos) and empathy with an emotive relational agent. We evaluated and compared the prototype to standard methods of communicating genetic risk information via a mixed methods approach that included surveys, questionnaires, interviews, observations, image analysis, and facial analysis. Main outcome variables were perceived ease of understanding, comprehension, emotional response, and motivation. We employed t-tests, ANOVAs, directed content analysis, correlation, regression, hierarchical clustering, and Chernoff faces to answer the research questions. All variables were significantly different for the prototype compared to the standard method, except for motivation as rated by 32 LHL, LEP community members. Findings revealed that LHL, LEP individuals have difficulty appropriately processing standard methods of communicating risk information, such as risk numbers supported by visual aids. Further, appealing visuals may inappropriately increase confidence in understanding of information. Visualizations affected emotions, which influenced perceived ease of understanding and motivation to take action on the information. Comprehension scores did not correlate with perceived ease of understanding, emotional response, or motivation. Findings suggest that providing access to comprehensible health information may not be enough to motivate patients to engage with their care; providing a good experience (taking into account the aesthetics and emotional response) of health information may be essential to optimize outcomes

American Gut: An Open Platform For Citizen Science Microbiome Research

Copyright © 2018 McDonald et al. Although much work has linked the human microbiome to specific phenotypes and lifestyle variables, data from different projects have been challenging to integrate and the extent of microbial and molecular diversity in human stool remains unknown. Using standardized protocols from the Earth Microbiome Project and sample contributions from over 10,000 citizen-scientists, together with an open research network, we compare human microbiome specimens primarily from the United States, United Kingdom, and Australia to one another and to environmental samples. Our results show an unexpected range of beta-diversity in human stool microbiomes compared to environmental samples; demonstrate the utility of procedures for removing the effects of overgrowth during room-temperature shipping for revealing phenotype correlations; uncover new molecules and kinds of molecular communities in the human stool metabolome; and examine emergent associations among the microbiome, metabolome, and the diversity of plants that are consumed (rather than relying on reductive categorical variables such as veganism, which have little or no explanatory power). We also demonstrate the utility of the living data resource and cross-cohort comparison to confirm existing associations between the microbiome and psychiatric illness and to reveal the extent of microbiome change within one individual during surgery, providing a paradigm for open microbiome research and education. IMPORTANCE We show that a citizen science, self-selected cohort shipping samples through the mail at room temperature recaptures many known microbiome results from clinically collected cohorts and reveals new ones. Of particular interest is integrating n = 1 study data with the population data, showing that the extent of microbiome change after events such as surgery can exceed differences between distinct environmental biomes, and the effect of diverse plants in the diet, which we confirm with untargeted metabolomics on hundreds of samples