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Articles 1 - 7 of 7
Full-Text Articles in Library and Information Science
Using Machine Learning To Predict Super-Utilizers Of Healthcare Services, Kevin Paul Buchan Jr.
Using Machine Learning To Predict Super-Utilizers Of Healthcare Services, Kevin Paul Buchan Jr.
Legacy Theses & Dissertations (2009 - 2024)
In this dissertation, I aim to forecast high utilizers of emergency care and inpatient Medicare services (i.e., healthcare visits). Through a literature review, I demonstrate that accurate and reliable prediction of these future high utilizers will not only reduce healthcare costs but will also improve the overall quality of healthcare for patients. By identifying this population at risk before manifestation, I propose that there is still time to reverse undesirable healthcare trajectories (i.e., individuals whose clinical risk increases an excessive healthcare and treatment burden) through timely attention and proper care coordination. My dissertation culminates in the delivery of state-of-the-art predictive …
Understanding Self-Managed Teams Using Biomimicking, Mohammad Nozari
Understanding Self-Managed Teams Using Biomimicking, Mohammad Nozari
Walden Dissertations and Doctoral Studies
AbstractThe potential high performance of self-managed teams can only materialize with implementing such teams properly and differently from traditional manager-led teams. This qualitative descriptive multiple case study presents biomimicking as a unique and untapped resource to achieve that potential by applying a biomimicking lens to help understand successful decision-making patterns for self-managed teams. The study population included team members of self-managed teams working in information technology companies in Toronto, Ontario, as the technology hub of Canada with a tendency to apply the latest approaches for teamwork performance and output. The conceptual framework of the study included teamwork, self-management, social choice, …
Citationally Enhanced Semantic Literature Based Discovery, John David Fleig
Citationally Enhanced Semantic Literature Based Discovery, John David Fleig
CCE Theses and Dissertations
We are living within the age of information. The ever increasing flow of data and publications poses a monumental bottleneck to scientific progress as despite the amazing abilities of the human mind, it is woefully inadequate in processing such a vast quantity of multidimensional information. The small bits of flotsam and jetsam that we leverage belies the amount of useful information beneath the surface. It is imperative that automated tools exist to better search, retrieve, and summarize this content. Combinations of document indexing and search engines can quickly find you a document whose content best matches your query - if …
Clinical Information Extraction From Unstructured Free-Texts, Mingzhe Tao
Clinical Information Extraction From Unstructured Free-Texts, Mingzhe Tao
Legacy Theses & Dissertations (2009 - 2024)
Information extraction (IE) is a fundamental component of natural language processing (NLP) that provides a deeper understanding of the texts. In the clinical domain, documents prepared by medical experts (e.g., discharge summaries, drug labels, medical history records) contain a significant amount of clinically-relevant information that is crucial to the overall well-being of patients. Unfortunately, in many cases, clinically-relevant information is presented in an unstructured format, predominantly consisting of free-texts, making it inaccessible to computerized methods. Automatic extraction of this information can improve accessibility. However, the presence of synonymous expressions, medical acronyms, misspellings, negated phrases, and ambiguous terminologies make automatic extraction …
Facilitating And Enhancing Biomedical Knowledge Translation: An In Silico Approach To Patient-Centered Pharmacogenomic Outcomes Research, Kourosh Ravvaz
Facilitating And Enhancing Biomedical Knowledge Translation: An In Silico Approach To Patient-Centered Pharmacogenomic Outcomes Research, Kourosh Ravvaz
Theses and Dissertations
Current research paradigms such as traditional randomized control trials mostly rely on relatively narrow efficacy data which results in high internal validity and low external validity. Given this fact and the need to address many complex real-world healthcare questions in short periods of time, alternative research designs and approaches should be considered in translational research. In silico modeling studies, along with longitudinal observational studies, are considered as appropriate feasible means to address the slow pace of translational research. Taking into consideration this fact, there is a need for an approach that tests newly discovered genetic tests, via an in silico …
Time Will Tell : Temporal Reasoning In Clinical Narratives And Beyond, Weiyi Sun
Time Will Tell : Temporal Reasoning In Clinical Narratives And Beyond, Weiyi Sun
Legacy Theses & Dissertations (2009 - 2024)
Temporal reasoning in natural language refers to the extraction and understanding of time-related information conveyed in free text. A clinical narrative temporal reasoning component can enable a spectrum of medical natural language processing (NLP) applications that directly improve patient care documentation efficiency, accessibility and accountability. This dissertation contributes in three subtasks under temporal reasoning: temporal annotation, temporal expression extraction and temporal relation inferences. The temporal annotation work described in the dissertation produced one of the first publicly available clinical narratives. We published one of the first sets of temporal
Automated Classification Of The Narrative Of Medical Reports Using Natural Language Processing, Ira J. Goldstein
Automated Classification Of The Narrative Of Medical Reports Using Natural Language Processing, Ira J. Goldstein
Legacy Theses & Dissertations (2009 - 2024)
In this dissertation we present three topics critical to the document level classification of the narrative in medical reports: the use of preferred terminology in light of the presence of synonymous terms, the less than optimal performance of classification systems when presented with a non-uniform distribution of classes, and the problems associated with scarcity of labeled data when presented with an imbalance of classes in the data sets.