Date of Award

Spring 2025

Document Type

Thesis

Publisher

Santa Clara : Santa Clara University, 2025

Department

Bioengineering

First Advisor

Hamed Akbari

Abstract

With the advent of COVID-19 pandemic global, healthcare systems were challenged with resource constraints plaguing hospital environments around the world with consequences that presented weaknesses in our healthcare system. This demonstrated the importance of triage in unprecedented scenarios. This project presents an integrated AI-driven diagnostic tool to predict the severity of COVID-19 cases using length of stay as an analog. By using clinical data and radiographic chest images, this project integrates a XGBoost clinical data model with a Convolutional Neural Network (CNN) model utilizing chest CT images to create a comprehensive assessment of patient case severity through predicted length of hospital stay. Publicly available data was preprocessed and analyzed using techniques such as minimum Redundance Maximum Relevance (mRMR) feature selection and Spearman’s Rank correlation. The performance of the models was evaluated through multiple statistical metrics including Concordance Index(C-index), F1-score, Receiver Operating Characteristics (ROC), and Area Under the Curve (AUC). Through our project we demonstrated improved performance using the combined imaging and clinical data models with a F1-score of 0.860 and a sensitivity of 0.958. This demonstrates the model’s utility in making patient management more efficient and paving the way to more personalized medicine in clinical settings in future work. These models could be expanded to handle other potential lung diseases such as Tuberculosis and Pneumonia.

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