Thumbnail Image
Item

Machine Learning in Healthcare: Improving the Diagnosis of Pulmonary Embolism in COVID-19 Patients

Osmani, Soheb
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.
Description
Date
2022-04-01
Journal Title
Journal ISSN
Volume Title
Publisher
Collections
Undergraduate Honors Theses
Show all metadata
Download Dataset
Files
0-OsmaniSohebcoversheet.pdf
Adobe PDF, 385.03 KB
Machine_Learning_in_Healthcare__Improving_the_Diagnosis.pdf
Adobe PDF, 7.23 MB
Rights Holder
Usage License
Embargo
Research Projects
Organizational Units
Journal Issue
Keywords
Citation
Advisor
Vasiliu, Daniel
Willner, Dana
Soloman, Jeffrey
Queen, Ashleigh
Department
Data Science
DOI
URI
https://scholarworks.wm.edu/handle/internal/11204
Embedded videos