Diabetes in humans is a long-term (chronic) illness that affects how our body converts food into energy. Approximately one in ten individuals residing in the United States is affected with diabetes and more than 90% of those have type 2 diabetes (T2D). Human bodies fail to produce insulin in type 1 diabetes, causing you to take insulin for survival. However, with type 2 diabetes, the body can't use insulin well. A proven way to manage diabetes is through a positive mindset and a healthy lifestyle. Several studies have been conducted at Virginia Tech and the University of Buffalo on discovering different helpful characteristics in a person's day-to-day life, which relate to important events. They consider Episodic Fu- ture Thinking (EFT), where participants identify several events/actions that might occur at multiple future time frames (1 month to 10 years) in text-based descriptions (cues). This re- search aims to detect content characteristics from these EFT cues. However, class imbalance often presents a challenging issue when dealing with such domain-specific data. To mitigate this issue, this research employs Elasticsearch to address data imbalance and enhance the machine learning (ML) pipeline for improved accuracy of predictions. By leveraging Elas- ticsearch and transformer models, this study constructs classifiers and regression models, which can be utilized to identify various content characteristics from the cues. To the best of our knowledge, this work represents the first such attempt to employ natural language processing (NLP) techniques to analyze EFT cues and establish a correlation between those characteristics and their impacts on decision-making and health outcomes. / Master of Science / Diabetes is a serious and long-term illness that impacts how the body converts food into energy. It affects around one in ten individuals residing in the United States, and over 90% of these individuals have type 2 diabetes (T2D). While a positive attitude and healthy lifestyle can help with management of diabetes, it is unclear exactly which mental attitudes most affect health outcomes. To gain a better understanding of this relationship, researchers from Virginia Tech and the University of Buffalo conducted multiple studies on Episodic Future Thinking (EFT), where participants identify several events or actions that could take place in the future. This research uses natural language processing (NLP) to analyze the descriptions of these events (cues) and identify different characteristics that relate to a person's day-to-day life. With the help of Elasticsearch and transformer models, this work handles the data imbalance and improves the model predictions for different categories within cues. Overall, this research has the potential to provide valuable insights that can impact their diabetes risk, potentially leading to better management and prevention strategies and treatments.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/115452 |
| Date | 16 June 2023 |
| Creators | Shah, Aditya Ashishkumar |
| Contributors | Computer Science and Applications, Fox, Edward A., Zhou, Dawei, Lourentzou, Ismini |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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