59 research outputs found
A framework for analyzing changes in health care lexicons and nomenclatures
Ontologies play a crucial role in current web-based biomedical applications for capturing contextual knowledge in the domain of life sciences. Many of the so-called bio-ontologies and controlled vocabularies are known to be seriously defective from both terminological and ontological perspectives, and do not sufficiently comply with the standards to be considered formai ontologies. Therefore, they are continuously evolving in order to fix the problems and provide valid knowledge. Moreover, many problems in ontology evolution often originate from incomplete knowledge about the given domain. As our knowledge improves, the related definitions in the ontologies will be altered. This problem is inadequately addressed by available tools and algorithms, mostly due to the lack of suitable knowledge representation formalisms to deal with temporal abstract notations, and the overreliance on human factors. Also most of the current approaches have been focused on changes within the internal structure of ontologies, and interactions with other existing ontologies have been widely neglected. In this research, alter revealing and classifying some of the common alterations in a number of popular biomedical ontologies, we present a novel agent-based framework, RLR (Represent, Legitimate, and Reproduce), to semi-automatically manage the evolution of bio-ontologies, with emphasis on the FungalWeb Ontology, with minimal human intervention. RLR assists and guides ontology engineers through the change management process in general, and aids in tracking and representing the changes, particularly through the use of category theory. Category theory has been used as a mathematical vehicle for modeling changes in ontologies and representing agents' interactions, independent of any specific choice of ontology language or particular implementation. We have also employed rule-based hierarchical graph transformation techniques to propose a more specific semantics for analyzing ontological changes and transformations between different versions of an ontology, as well as tracking the effects of a change in different levels of abstractions. Thus, the RLR framework enables one to manage changes in ontologies, not as standalone artifacts in isolation, but in contact with other ontologies in an openly distributed semantic web environment. The emphasis upon the generality and abstractness makes RLR more feasible in the multi-disciplinary domain of biomedical Ontology change management
Managing Requirement Volatility in an Ontology-Driven Clinical LIMS Using Category Theory. International Journal of Telemedicine and Applications
Requirement volatility is an issue in software engineering in general, and in
Web-based clinical applications in particular, which often originates from an
incomplete knowledge of the domain of interest. With advances in the health
science, many features and functionalities need to be added to, or removed
from, existing software applications in the biomedical domain. At the same
time, the increasing complexity of biomedical systems makes them more difficult
to understand, and consequently it is more difficult to define their
requirements, which contributes considerably to their volatility. In this
paper, we present a novel agent-based approach for analyzing and managing
volatile and dynamic requirements in an ontology-driven laboratory information
management system (LIMS) designed for Web-based case reporting in medical
mycology. The proposed framework is empowered with ontologies and formalized
using category theory to provide a deep and common understanding of the
functional and nonfunctional requirement hierarchies and their interrelations,
and to trace the effects of a change on the conceptual framework.Comment: 36 Pages, 16 Figure
Fine-tuned Sentiment Analysis of COVID-19 Vaccine-Related Social Media Data: Comparative Study
This study investigated and compared public sentiment related to COVID-19
vaccines expressed on two popular social media platforms, Reddit and Twitter,
harvested from January 1, 2020, to March 1, 2022. To accomplish this task, we
created a fine-tuned DistilRoBERTa model to predict sentiments of approximately
9.5 million Tweets and 70 thousand Reddit comments. To fine-tune our model, our
team manually labeled the sentiment of 3600 Tweets and then augmented our
dataset by the method of back-translation. Text sentiment for each social media
platform was then classified with our fine-tuned model using Python and the
Huggingface sentiment analysis pipeline. Our results determined that the
average sentiment expressed on Twitter was more negative (52% positive) than
positive and the sentiment expressed on Reddit was more positive than negative
(53% positive). Though average sentiment was found to vary between these social
media platforms, both displayed similar behavior related to sentiment shared at
key vaccine-related developments during the pandemic. Considering this similar
trend in shared sentiment demonstrated across social media platforms, Twitter
and Reddit continue to be valuable data sources that public health officials
can utilize to strengthen vaccine confidence and combat misinformation. As the
spread of misinformation poses a range of psychological and psychosocial risks
(anxiety, fear, etc.), there is an urgency in understanding the public
perspective and attitude toward shared falsities. Comprehensive educational
delivery systems tailored to the population's expressed sentiments that
facilitate digital literacy, health information-seeking behavior, and precision
health promotion could aid in clarifying such misinformation.Comment: 11 Pages, 5 Figures, and 1 Tabl
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