Theses and Dissertations

Date of Award

5-2025

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Applied Statistics and Data Science

First Advisor

Xuan Wang

Second Advisor

Tamer Oraby

Third Advisor

Hansapani Rodrigo

Abstract

The widespread misuse and excessive prescription of antibiotics have played a pivotal role in the emergence and proliferation of antibiotic-resistant bacteria, posing a critical global public health crisis. Addressing this challenge necessitates innovative solutions that enhance antimicrobial stewardship. This study presents the development and implementation of a visual decision support system designed to monitor and optimize antibiotic usage among healthcare providers. The proposed system integrates advanced machine learning algorithms with real-time data analytics to provide a dynamic, evidence-based decision support tool. Specifically, a neural network model was developed after evaluating multiple machine learning approaches, including Random Forest, Logistic Regression and XGBoost with the neural network ultimately demonstrating superior predictive performance. The models were deployed using TabPy to facilitate seamless integration with Tableau. Tableau served as the primary visualization platform, enabling intuitive and interactive representations of antibiotic prescribing patterns. The system was designed to incorporate contextualized warning and an AI-powered model to support proactive decision-making by generating predictive insights and alert notifications within Tableau.

Comments

Copyright 2025 Akua Sekyiwaa Osei-Nkwantabisa. https://proquest.com/docview/3240635261

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