Informatics School Theses and Dissertations

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Please go to "Informatics Graduate Theses and PhD Dissertations" to submit dissertations and theses for the School of Informatics and Computing, at: http://hdl.handle.net/1805/303.

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A Model of Project Continuation in Game Jams and Hackathons
(2024-08) Faas, Travis Byron; Miller, Andrew; Dombrowski, Lynn; Brady, Erin; Hickey, Daniel
Game jams and hackathons are events where individuals design and build new technology prototypes in a short timeframe. Prototypes made at hackathons are often abandoned after the event and are never finished or used by their intended audiences. Though continued work on prototypes is not the only goal of hackathons, many expect that some hackathon projects will continue to be developed to fulfill the civic, educational, or entrepreneurial goals of hackathon organizers and attendees. To assist hackathon organizers in running hackathons that produce continued projects, I present in this document a model of project continuation in online hackathons and a tool that directs conversations that develops the necessary components of continuation. This model was developed through three studies: a design study that generated the design for a bot to be used in an online game jam that directs users in breaking the boundedness of their game concept, a deployment study where the bot was deployed and used in an online game jam, and a longitudinal study that followed the continuation practices of individuals who used the bot during the jam. In the presented continuation model, I highlight how recent personal interests generate an extended development context that reduces the boundedness of game jams and show how regular sharing and discussion of progress creates social investment in the success of projects that contributes to continuation intention and support. This continuation model requires a resting period post-hackathon, which sometimes generates conceptual continuation where a project is abandoned but the major project concepts are explored in later projects. Taking this idea of concept continuation further, I offer suggestions on how to gain continuation in hackathons by reducing their time-boundedness and making the events more permeable to allow for prior-existing projects to be accepted and further developed at these events.
Academic laboratory information management system: a tool for science and computer science students
(2011-07-08) Lerch, Spencer; Merchant, Mahesh; Wild, David; Doman, Thompson N.
Proof of Concept - An Academic LIMS application: The aim of this project is the creation of an open-source, freeware LIMS application that can be used in an academic setting as a teaching tool for both chemistry and computer science students. The LIMS package will combine an application, developed using VB.NET, to manage the data with other open-source or freeware programs such as MySQL and WEKA. The numerous commercial chemical informatics applications available are useful tools to learn how to manage data from a user's standpoint. However, they are not readily available to the average student, nor do they offer a great understanding into how they were developed from a programmer's frame of mind. There is a great void here that, if filled can greatly help the academic community.
Acceptance of use of personal health record: factors affecting physicians' perspective
(2011-10-19) Agrawal, Ekta; Jones, Josette F.; Weiner, Michael; Simmermaker, Jennifer
Acceptance of PHR by physicians is fundamental as they play important role towards the promotion of PHR adoption by providing the access to the data to be maintained in PHR and also, using the information within the PHR for decision making. Therefore it is important to measure physicians' perspective on usefulness of PHR, and also the value and trust they have in PHR usage. Review of previous researches identifies the lack of availability of a valid survey instrument that can be used to measure physicians' perception on all different aspects of PHR use and acceptance. Using the integrated literature review methodology and Unified Theory of Acceptance and Use of Technology (UTAUT) as a guiding framework, this study was aimed to identify the factors that can be used in the development of comprehensive evaluation instrument to understand physicians' acceptance of PHR. Total 15 articles were selected for literature review and using the content analysis method, 189 undifferentiated data units were extracted from those articles. These data units were then categorized into the four core constructs of UTAUT. ―Other categorization system was also created for the data units that could not be classified into one of the UTAUT core constructs. Among four core UTAUT constructs, Performance Expectancy is found to be the most influential factor in physicians' acceptance of PHR, followed by ―Other factors, Facilitating Condition and Social Influence. Effort expectancy was found to be the least influential. The identified specific factors within each domain can be used to develop a valid survey instrument to measure physicians' perception on PHR.
ACLRO: An Ontology for the Best Practice in ACLR Rehabilitation
(2020-10) Phalakornkule, Kanitha; Jones, Josette F.; Boukai, Ben; Liu, Xiaowen; Purkayatha, Saptarshi; Duncan, William D.
With the rise of big data and the demands for leveraging artificial intelligence (AI), healthcare requires more knowledge sharing that offers machine-readable semantic formalization. Even though some applications allow shared data interoperability, they still lack formal machine-readable semantics in ICD9/10 and LOINC. With ontology, the further ability to represent the shared conceptualizations is possible, similar to SNOMED-CT. Nevertheless, SNOMED-CT mainly focuses on electronic health record (EHR) documenting and evidence-based practice. Moreover, due to its independence on data quality, the ontology enhances advanced AI technologies, such as machine learning (ML), by providing a reusable knowledge framework. Developing a machine-readable and sharable semantic knowledge model incorporating external evidence and individual practice’s values will create a new revolution for best practice medicine. The purpose of this research is to implement a sharable ontology for the best practice in healthcare, with anterior cruciate ligament reconstruction (ACLR) as a case study. The ontology represents knowledge derived from both evidence-based practice (EBP) and practice-based evidence (PBE). First, the study presents how the domain-specific knowledge model is built using a combination of Toronto Virtual Enterprise (TOVE) and a bottom-up approach. Then, I propose a top-down approach using Open Biological and Biomedical Ontology (OBO) Foundry ontologies that adheres to the Basic Formal Ontology (BFO)’s framework. In this step, the EBP, PBE, and statistic ontologies are developed independently. Next, the study integrates these individual ontologies into the final ACLR Ontology (ACLRO) as a more meaningful model that endorses the reusability and the ease of the model-expansion process since the classes can grow independently from one another. Finally, the study employs a use case and DL queries for model validation. The study's innovation is to present the ontology implementation for best-practice medicine and demonstrate how it can be applied to a real-world setup with semantic information. The ACLRO simultaneously emphasizes knowledge representation in health-intervention, statistics, research design, and external research evidence, while constructing the classes of data-driven and patient-focus processes that allow knowledge sharing explicit of technology. Additionally, the model synthesizes multiple related ontologies, which leads to the successful application of best-practice medicine.
ACTIVE READING ON TABLET TEXTBOOKS
(2015-04-17) Palilonis, Jennifer Ann; Defazio, Joseph; Bolchini, Davide; Butler, Darrell; Voida, Amy
To study a text, learners often engage in active reading. Through active reading, learners build an analysis by annotating, outlining, summarizing, reorganizing and synthesizing information. These strategies serve a fundamental meta-cognitive function that allows content to leave strong memory traces and helps learners reflect, understand, and recall information. Textbooks, however, are becoming more complex as new technologies change how they are designed and delivered. Interactive, touch-screen tablets offer multi-touch interaction, annotation features, and multimedia content as a browse-able book. Yet, such tablet textbooks-in spite of their increasing availability in educational settings-have received little empirical scrutiny regarding how they support and engender active reading. To address this issue, this dissertation reports on a series of studies designed to further our understanding of active reading with tablet textbooks. An exploratory study first examined strategies learners enact when reading and annotating in the tablet environment. Findings indicate learners are often distracted by touch screen mechanics, struggle to effectively annotate information delivered in audiovisuals, and labor to cognitively make connections between annotations and the content/media source from which they originated. These results inspired SMART Note, a suite of novel multimedia annotation tools for tablet textbooks designed to support active reading by: minimizing interaction mechanics during active reading, providing robust annotation for multimedia, and improving built-in study tools. The system was iteratively developed through several rounds of usability and user experience evaluation. A comparative experiment found that SMART Note outperformed tablet annotation features on the market in terms of supporting learning experience, process, and outcomes. Together these studies served to extend the active reading framework for tablet textbooks to: (a) recognize the tension between active reading and mechanical interaction; (b) provide designs that facilitate cognitive connections between annotations and media formats; and (c) offer opportunities for personalization and meaningful reorganization of learning material.
Advanced natural language processing and temporal mining for clinical discovery
(2015-08-17) Mehrabi, Saeed; Jones, Josette F.; Palakal, Mathew J.; Chien, Stanley Yung-Ping; Liu, Xiaowen; Schmidt, C. Max
There has been vast and growing amount of healthcare data especially with the rapid adoption of electronic health records (EHRs) as a result of the HITECH act of 2009. It is estimated that around 80% of the clinical information resides in the unstructured narrative of an EHR. Recently, natural language processing (NLP) techniques have offered opportunities to extract information from unstructured clinical texts needed for various clinical applications. A popular method for enabling secondary uses of EHRs is information or concept extraction, a subtask of NLP that seeks to locate and classify elements within text based on the context. Extraction of clinical concepts without considering the context has many complications, including inaccurate diagnosis of patients and contamination of study cohorts. Identifying the negation status and whether a clinical concept belongs to patients or his family members are two of the challenges faced in context detection. A negation algorithm called Dependency Parser Negation (DEEPEN) has been developed in this research study by taking into account the dependency relationship between negation words and concepts within a sentence using the Stanford Dependency Parser. The study results demonstrate that DEEPEN, can reduce the number of incorrect negation assignment for patients with positive findings, and therefore improve the identification of patients with the target clinical findings in EHRs. Additionally, an NLP system consisting of section segmentation and relation discovery was developed to identify patients' family history. To assess the generalizability of the negation and family history algorithm, data from a different clinical institution was used in both algorithm evaluations.
Advancing Toxicology-Based Cancer Risk Assessment with Informatics
(2010-05-03T19:38:33Z) Bercu, Joel P.; Mahoui, Malika; Romero, Pedro R.; Stevens, James L.; Jones, Josette F.; Palakal, Mathew J.
Since exposure to carcinogens can occur in the environment from various point sources, cancer risk assessment attempts to define and limit potential exposure such that the risk of developing cancer is negligible. While cancer risk assessment is widely used with certain methodologies well accepted in the scientific literature and regulatory guidances, there are still gaps which increase uncertainties when assessing risk including: (1) mixtures of genotoxins, (2) genotoxic metabolites, and (3) nongenotoxic carcinogens. An in silico model was developed to predict the cancer risk of a genotoxin which improved methodology for a single compound and mixtures. Monte Carlo simulations performed with a carcinogenicity potency database to estimate the overall carcinogenic risk of a mixture of genotoxic compounds showed that structural similarity would not likely increase the overall cancer risk. A cancer risk model was developed for genotoxic metabolites using excretion material in both animals and humans to determine the probability not exceeding a 1 in 100,000 excess cancer risk. Two model nongenotoxic compounds (fenofibrate and methapyraline) were tested in short-term microarray studies to develop a framework for cancer risk assessment. It was determined that a threshold for potential key events could be derived using benchmark dose analysis in combination with well developed ontologies (Kegg/GO), which were at or below measured tumorigenic and precursor events. In conclusion, informatics was effective in advancing toxicology-based cancer risk assessment using databases and predictive techniques which fill critical gaps in its methodology.
Advocacy in Mental Health Social Interactions on Public Social Media
(2022-02) Cornet, Victor P.; Holden, Richard J.; Bolchini, Davide; Brady, Erin; Mohler, George; Hong, Michin; Lee, Sangwon
Health advocacy is a social phenomenon in which individuals and collectives attempt to raise awareness and change opinions and policies about health-related causes. Mental health advocacy is health advocacy to advance treatment, rights, and recognition of people living with a mental health condition. The Internet is reshaping how mental health advocacy is performed on a global scale, by facilitating and broadening the reach of advocacy activities, but also giving more room for opposing mental health advocacy. Another factor contributing to mental health advocacy lies in the cultural underpinnings of mental health in different societies; East Asian countries like South Korea have higher stigma attached to mental health compared to Western countries like the US. This study examines interactions about schizophrenia, a specific mental health diagnosis, on public social media (Facebook, Instagram, and Twitter) in two different languages, English and Korean, to determine how mental health advocacy and its opposition are expressed on social media. After delineation of a set of keywords for retrieval of content about schizophrenia, three months’ worth of social media posts were collected; a subset of these posts was then analyzed qualitatively using constant comparing with a proposed model describing online mental heath advocacy based on existing literature. Various expressions of light mental health advocacy, such as sharing facts about schizophrenia, and strong advocacy, showcasing offline engagement, were found in English posts; many of these expressions were however absent from the analyzed Korean posts that heavily featured jokes, insults, and criticisms. These findings were used to train machine learning classifiers to detect advocacy and counter-advocacy. The classifiers confirmed the predominance of counter-advocacy in Korean posts compared to important advocacy prevalence in English posts. These findings informed culturally sensitive recommendations for social media uses by mental health advocates and implications for international social media studies in human-computer interaction.
aiDance: A Non-Invasive Approach in Designing AI-Based Feedback for Ballet Assessment and Learning
(2021-12) Trajkova, Milka; Cafaro, Francesco; Bolchini, Davide; Dombrowski, Lynn; Fusco, Judi; Hickey, Daniel; Magerko, Brian; Toenjes, John
Since its codified genesis in the 18th century, ballet training has largely been unchanged: it relies on tools that lack adequate support for both dancers and teachers. In particular, providing effective augmented feedback remains challenging as it can be limited, not always provided at the proper time, and highly subjective as it depends on the visual experience of an instructor. Designing a ballet assessment and learning tool with the aim of achieving a meaningful educational experience is an interdisciplinary challenge due to the fine motor movements and patterns of the art form. My work examines how we can effectively augment ballet learning in three phases using mixedmethod approaches. First, through my past professional experience as a ballet dancer, I explore how the design and in-lab evaluation of augmented visual and verbal feedback can improve the technical performance for novices and experts via remote learning. Second, I investigate the learning and teaching challenges that currently exist in traditional in-person training environments for dancers and teachers. Furthermore, I study the current technology use, reasons for non-use, and derive design requirements for future use. Lastly, I focus on how we can design aiDance, an AI-based feedback tool that attempts to represent an affordable and non-invasive approach that augments teachers’ abilities to facilitate assessment in the 21st century and pirouette towards the enhancement of learning. With this empirical work, I present insights that inform the HCI community at the intersection of dance and design in addressing the first steps towards the standardization of motor learning feedback.
Ambulatory Computerized Provider Order Entry and PDA-Based Clinical Decision Support Systems: An Investigation of their Patient Safety Effectiveness via an Integrative and Systematic Review
Taffel, Jared Ross; Jones, Josette F.
Substantial research has been done on inpatient provider order entry systems with varying degrees of clinical decision support. Such studies have examined how these technologies impact patient safety as well as the quality and cost of care. However, given that most medical care and prescriptions are administered in an ambulatory setting, the dearth of research on ACPOE systems is quite astonishing. This knowledge gap demonstrates the need for an integrative and systematic literature review that attempts to assess the research done on computerized patient safety interventions in ambulatory care. This review’s findings provided adequate evidence that ACPOE systems are effective interventions for reducing medication errors. Other evidence further indicated that, in terms of functional capabilities, commercial ACPOE and e-prescribing systems may be catching up with their homegrown counterparts. PDA-based CDSSs were depicted as useful tools for raising adherence to guidelines and inducing safer prescribing. These findings suggest that ACPOE And PDA-based CDS systems show promise for improving safety and healthcare quality in ambulatory settings. ACPOE specifically, tended to have more advanced CDS attributes but, nonetheless, showed more negative results compared to the e-prescribing systems. Close scrutiny should therefore be given to the elements of decision support that ambulatory physicians find most useful.

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