Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
Displaying 1 - 6 of 6
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- All Subjects: artificial intelligence
- Creators: Harrington Bioengineering Program
Description
Carbohydrate counting has been shown to improve HbA1c levels for people with diabetes. However, the learning curve and inconvenience of carbohydrate counting make it difficult for patients to adhere to it. A deep learning model is proposed to identify food from an image, where it can help the user manage their carbohydrate counting. This early model has a 68.3% accuracy of identifying 101 different food classes. A more refined model in future work could be deployed into a mobile application to identify food the user is about to consume and log it for easier carbohydrate counting.
ContributorsCarreto, Cesar (Author) / Pizziconi, Vincent (Thesis director) / Vernon, Brent (Committee member) / Harrington Bioengineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
The purpose of this investigation is to apply a machine learning algorithm with de-identified, historic oncology clinical trial data to assess the theoretical understanding of predictive modeling to derive potential clinical practice recommendations. Within this study, electronic medical records from the HonorHealth Virginia G. Piper Institute will undergo data visualization to identify potential correlations and trends critical for model creation as well as further identify potential expansions or limitations of scope regarding model purpose. Hypothesis pursued post data visualization was the development of a predictive model for 6-month survival. Current standard is estimated physician accuracy at 56.5% accuracy at 6 months out. This study created supervised learning models using decision trees, KNN, SVM and Ensemble methods using combinations of LASSO Logistic Regression and Know-GRFF Random Forest for feature selection. SVM trained on a combined set of LASSO and Know-GRRF featured produced the highest performing model at 75.5% with an AUC of 0.82. This study demonstrates the potential for applying predictive modeling on readily available EMR records to drive clinical practice recommendations. The models developed could potentially, with further development, be used as an ancillary tool for jumpstarting patient-physician conversations on survival and life expectancy.
ContributorsLi, Richard Longfei (Co-author) / Liu, Li (Co-author, Thesis director) / Gosselin, Kevin (Co-author, Committee member) / Harrington Bioengineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
The adaptive artificial-intelligence (AI) medical device industry is a novel industry in the United States offering innovations to the healthcare field. The rapid expansion of this industry in recent years has drawn attention from multiple stakeholders causing a heated debate about how to introduce these innovations into the market while maintaining patient safety and treatment efficacy. Since early 2019, the U.S. Food and Drug Administration (FDA) has been releasing statements in regards to the improvement of regulation for this new technology, but has yet to take further actions. Dilemmas including 1) a difficult regulatory process, 2) a heightening financial burden and 3) looming liability issues, are reasons adaptive AI medical devices have struggled to be advanced. By conducting a thorough analysis of these 3 issues, recognizing the intricacies of them separately and together, this study develops a better understanding of the landscape adaptive AI technology is facing and provides a clearer picture for the future of the industry.
ContributorsOgden, Ravyn Nicole (Author) / Coursen, Jerry (Thesis director) / Pizziconi, Vincent (Committee member) / Harrington Bioengineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
Artificial intelligence (AI) and machine learning (ML) algorithms are revolutionizing the field of healthcare by offering new opportunities for improved diagnosis and treatment planning. These technologies have the potential to transform the way medical professionals approach patient care by analyzing vast amounts of data, identifying patterns, and making predictions. This overview highlights the current state of research and development in the field of AI and ML for diagnosis and treatment planning, as well as explore the ethical benefits and challenges associated with their implementation.
ContributorsShankar, Kruthy (Author) / Arquiza, Jose (Thesis director) / Sobrado, Michael (Committee member) / Barrett, The Honors College (Contributor) / Harrington Bioengineering Program (Contributor)
Created2024-05
ContributorsShankar, Kruthy (Author) / Arquiza, Jose (Thesis director) / Sobrado, Michael (Committee member) / Barrett, The Honors College (Contributor) / Harrington Bioengineering Program (Contributor)
Created2024-05
ContributorsShankar, Kruthy (Author) / Arquiza, Jose (Thesis director) / Sobrado, Michael (Committee member) / Barrett, The Honors College (Contributor) / Harrington Bioengineering Program (Contributor)
Created2024-05