A Systems-Based Patient Aid Design Artifact for Active Medication Management in Type 2 Diabetes

In this dissertation, I explore the use of the Abstraction-Decomposition Space (ADS) alongside Hierarchical Task Analysis (HTA) to guide the design of a minimalist patient aid for active medication management in type 2 diabetes. The goal is to address a practical problem, but in addition, this study seeks to address a theoretical problem that is prevalent in design research in Information Systems (IS) today. The practical problem concerns the need for IT-based care delivery models to support patients in the interim period between in-person visits. In this vein, I present a bare-minimum design that focuses on the most essential functionality required to achieve remote insulin titration using the ADS and HTA. The theoretical problem, on the other hand, pertains to the limitations resulting from taking a tool-focused view in design research which inhibits our ability to produce generalized knowledge about IT systems in their contexts. The study proposes an alternative view based on work systems. The overarching thesis is that a work systems view provides for knowledge at a more abstract and generalizable level, yielding contributions beyond mere software packages. Moreover, the study highlights the artifact-building methodology used to delineate the rationale behind the design and to balance evaluation-dominant design research. In this vein, I conducted document analysis and semi-structured interviews with patients and care providers to develop the ADS, then used it alongside HTA to develop and test the usability of twelve user scenarios implemented on a large mobile form factor

Narrative Means to Preventative Ends: A Narrative Engagement Framework For Designing Prevention Interventions

This article describes a Narrative Engagement Framework (NEF) for guiding communication-based prevention efforts. This framework suggests that personal narratives have distinctive capabilities in prevention. The article discusses the concept of narrative, links narrative to prevention, and discusses the central role of youth in developing narrative interventions. As illustration, the authors describe how the NEF is applied in the keepin\u27 it REAL adolescent drug prevention curriculum, pose theoretical directions, and offer suggestions for future work in prevention communication

Coherence in hybrid texts: The case of the Patient Information Leaflet genre

This article investigates the degree of coherence in a particular type of hybrid text, viz. the Patient Information Leaflet, which is a lay-oriented genre fusing original and derived textual elements. Having their source in the specialized genre named Summary of Product Characteristics, derived segments undergo various kinds and degrees of registerial simplification in the transfer to the Patient Information Leaflet. Given this complex textual genesis, the aim of the article is to investigate the nature and degree of coherence in the Patient Information Leaflet genre, and, more specifically, to investigate the degree of integration between derived and non-derived elements. Following Halliday/Hasan’s (1976) definition of the coherence concept, the article examines the genre’s contextual coherence in the form of registerial consistency and its text-internal coherence in the form of cohesiveness. For the investigation of cohesiveness, the analytical framework known as Cohesive Harmony Analysis (Hasan 1984, 1985) has been applied to a sample text from the genre, and likewise to a parallel lay-oriented, but non-hybrid text from the health column of a British quality newspaper, which serves as a control. While the investigation confirms already well-known problems with registerial consistency in the Patient Information Leaflet genre, the analytical results reflect a high degree of cohesiveness, and one that is even markedly higher than that of the non-hybrid control text. Further, the results reflect a high level of integration between derived and non-derived segments in the Patient Information Leaflet

Coherence in hybrid texts:The case of the Patient Information Leaflet genre

This article investigates the degree of coherence in a particular type of hybrid text, viz. the Patient Information Leaflet, which is a lay-oriented genre fusing original and derived textual elements. Having their source in the specialized genre named Summary of Product Characteristics, derived segments undergo various kinds and degrees of registerial simplification in the transfer to the Patient Information Leaflet. Given this complex textual genesis, the aim of the article is to investigate the nature and degree of coherence in the Patient Information Leaflet genre, and, more specifically, to investigate the degree of integration between derived and non-derived elements. Following Halliday/Hasan’s (1976) definition of the coherence concept, the article examines the genre’s contextual coherence in the form of registerial consistency and its text-internal coherence in the form of cohesiveness. For the investigation of cohesiveness, the analytical framework known as Cohesive Harmony Analysis (Hasan 1984, 1985) has been applied to a sample text from the genre, and likewise to a parallel lay-oriented, but non-hybrid text from the health column of a British quality newspaper, which serves as a control. While the investigation confirms already well-known problems with registerial consistency in the Patient Information Leaflet genre, the analytical results reflect a high degree of cohesiveness, and one that is even markedly higher than that of the non-hybrid control text. Further, the results reflect a high level of integration between derived and non-derived segments in the Patient Information Leaflet

ESSAY: A “SAFETY MODEL” PERSPECTIVE CAN AID DIAGNOSIS, PREVENTION, AND RESTORATION AFTER CRIMINAL JUSTICE HARMS

ESSAY: A “SAFETY MODEL” PERSPECTIVE CAN AID DIAGNOSIS, PREVENTION, AND RESTORATION AFTER CRIMINAL JUSTICE HARM

Interactive Machine Learning with Applications in Health Informatics

Recent years have witnessed unprecedented growth of health data, including millions of biomedical research publications, electronic health records, patient discussions on health forums and social media, fitness tracker trajectories, and genome sequences. Information retrieval and machine learning techniques are powerful tools to unlock invaluable knowledge in these data, yet they need to be guided by human experts. Unlike training machine learning models in other domains, labeling and analyzing health data requires highly specialized expertise, and the time of medical experts is extremely limited. How can we mine big health data with little expert effort? In this dissertation, I develop state-of-the-art interactive machine learning algorithms that bring together human intelligence and machine intelligence in health data mining tasks. By making efficient use of human expert's domain knowledge, we can achieve high-quality solutions with minimal manual effort. I first introduce a high-recall information retrieval framework that helps human users efficiently harvest not just one but as many relevant documents as possible from a searchable corpus. This is a common need in professional search scenarios such as medical search and literature review. Then I develop two interactive machine learning algorithms that leverage human expert's domain knowledge to combat the curse of "cold start" in active learning, with applications in clinical natural language processing. A consistent empirical observation is that the overall learning process can be reliably accelerated by a knowledge-driven "warm start", followed by machine-initiated active learning. As a theoretical contribution, I propose a general framework for interactive machine learning. Under this framework, a unified optimization objective explains many existing algorithms used in practice, and inspires the design of new algorithms.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147518/1/raywang_1.pd

Transdisciplinary AI Observatory -- Retrospective Analyses and Future-Oriented Contradistinctions

In the last years, AI safety gained international recognition in the light of heterogeneous safety-critical and ethical issues that risk overshadowing the broad beneficial impacts of AI. In this context, the implementation of AI observatory endeavors represents one key research direction. This paper motivates the need for an inherently transdisciplinary AI observatory approach integrating diverse retrospective and counterfactual views. We delineate aims and limitations while providing hands-on-advice utilizing concrete practical examples. Distinguishing between unintentionally and intentionally triggered AI risks with diverse socio-psycho-technological impacts, we exemplify a retrospective descriptive analysis followed by a retrospective counterfactual risk analysis. Building on these AI observatory tools, we present near-term transdisciplinary guidelines for AI safety. As further contribution, we discuss differentiated and tailored long-term directions through the lens of two disparate modern AI safety paradigms. For simplicity, we refer to these two different paradigms with the terms artificial stupidity (AS) and eternal creativity (EC) respectively. While both AS and EC acknowledge the need for a hybrid cognitive-affective approach to AI safety and overlap with regard to many short-term considerations, they differ fundamentally in the nature of multiple envisaged long-term solution patterns. By compiling relevant underlying contradistinctions, we aim to provide future-oriented incentives for constructive dialectics in practical and theoretical AI safety research

An Assistive Tool for Authoring Visualization Thumbnails

Department of Computer Science and EngineeringVisualization of data is continuously stimulating for its potential to describe narratives inside data. It is a well-known medium in the digital era for expressing the insights of data. In recent times, storytelling in data-driven articles that comes under the category of data journalism is significantly adapting by news organizations. However, current data-driven visualization thumbnail tools either are lacking support for extracting the information from documents that contain unstructured text, tables, and graphical data and telling the story on it or require expressive technical expertise. Therefore, I introduce an integrated authoring tool, which is a combination of model and user interface. The objective of this study is to simplify the informative thumbnail creation process in the field of journalism. Generally, the current prevailing systems involve manually selecting and formatting entity from textual or tabular source, a process that leads to being tiresome and error-prone. Furthermore, there is no tool exist that extracts the insights from the document???s unstructured text, tables and graphics data simultaneously and provides graphical visuals for thumbnail or static visualization. With VTComp, data-driven news article contents are automatically extracted and converted into graphics and formatted textual layout, to enable journalists for further usage of results. We presented a user interface, which consists of all the essential components required for narrative visualization. Our system expresses the data insights with separate categories like summary text, graphical response view which contains chart visuals and document related visuals differentiated by label text and the final output of the system is interactive static visual graphics contributed by machine and user. By enabling storytelling without programming, the VTComp interface overcomes the interaction gap between user and system-generated results. We evaluated VTComp through multiple measurements such as benchmark comparison of automatically extraction of target entities against manual extraction, and system compatibility with different news organizations??? data-driven articles. Besides, an introductory evaluation of the user experience of thumbnail authoring using iPad and touch pencil by performing user study session and a follow-up quantitative and qualitative analysis. Finally, The results of the user study acknowledge that VTComp beneficial for journalists to create the data-rich and informative graphics, thumbnails from an unstructured text document with-in a short period, ithout any special expertise and efforts.clos