Date Thesis Awarded

4-2022

Access Type

Honors Thesis -- Open Access

Degree Name

Bachelors of Science (BS)

Department

Data Science

Advisor

Daniel Vasiliu

Committee Members

Dana Willner

Jeffrey Soloman

Ashleigh Queen

Abstract

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has created new challenges for clinicians diagnosing pulmonary embolism (PE). Clinicians currently rely on D-Dimer levels in conjunction with clinical prediction scores to rule out and diagnose PE. However, patients with COVID-19 (the disease caused by SARS-CoV-2) often present with elevated D-Dimer levels. D-Dimer levels in COVID-19 patients have been found to be positively correlated with the severity of disease. Symptoms of COVID-19 also often align with symptoms of PE. Therefore, it becomes more difficult for clinicians to identify which COVID-19 positive patients should undergo further testing for PE. This study evaluates the accuracy of current diagnostic criteria for PE and proposes more flexible machine learning models based on the patient’s Wells Score, D-dimer, and other important covariates (i.e., age, gender, and COVID status) to be used in conjunction with pre existing criteria.

Recommended Citation

Osmani, Soheb, "Machine Learning in Healthcare: Improving the Diagnosis of Pulmonary Embolism in COVID-19 Patients" (2022). Undergraduate Honors Theses. William & Mary. Paper 1764.
https://scholarworks.wm.edu/honorstheses/1764