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Making Sense Of Online Public Health Debates With Visual Analytics Systems, Anton Ninkov
Making Sense Of Online Public Health Debates With Visual Analytics Systems, Anton Ninkov
Electronic Thesis and Dissertation Repository
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 …
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
Tagline: Information Extraction For Semi-Structured Text Elements In Medical Progress Notes, Dezon K. Finch
Tagline: Information Extraction For Semi-Structured Text Elements In Medical Progress Notes, Dezon K. Finch
USF Tampa Graduate Theses and Dissertations
Text analysis has become an important research activity in the Department of Veterans Affairs (VA). Statistical text mining and natural language processing have been shown to be very effective for extracting useful information from medical documents. However, neither of these techniques is effective at extracting the information stored in semi-structure text elements. A prototype system (TagLine) was developed as a method for extracting information from the semi-structured portions of text using machine learning. Features for the learning machine were suggested by prior work, as well as by examining the text, and selecting those attributes that help distinguish the various classes …