Conference paper
A Coronavirus Cohort Case Study - Dataset Trends using Machine Learning Methods
Proceedings (IEEE International Conference on Bioinformatics and Biomedicine), pp 4213-4219
05 Dec 2023
Abstract
In this cohort study, we analyzed data collected from Drexel University students, faculty, and staff (age 18 - 79) using machine learning models to gain insight into the significance and predictive capabilities of the the features collected. Data from 126,983 SARS-CoV-2 tests was collected from 16,914 unique individuals from March 4, 2020 to April 24, 2022. Associated symptom data (551,257 reports) was collected through the Drexel University Health Checker App powered by the industry partner, Respond Health. 4,457 people had a positive SARS-CoV-2 test result within the study timeframe. 2,074 of the 4,457 (46.53%) positive cases were in individuals that were fully vaccinated. Given the comprehensive data collected over the entire period of the pandemic, we are able to explore the trends and importance of features to their predictive capabilities. In our experiments and results, we analyze the relative importance of the collected features during different time periods of the COVID-19 evolution and present the trends over time.
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Details
- Title
- A Coronavirus Cohort Case Study - Dataset Trends using Machine Learning Methods
- Creators
- Edward Kim - Drexel University, Computer Science (Computing)Isamu Isozaki - Drexel University, College of Computing and InformaticsSatvik Tripathi - Drexel University, Computer Science (Computing)Lucy Robinson - Drexel University, Epidemiology and BiostatisticsNoreen Robertson - Drexel University, SOM Dean - Research AdministrationVicki Seyfert-Margolis - Respond Health,Bethesda,MD,USACharles B. Cairns - Drexel University, College of Medicine
- Publication Details
- Proceedings (IEEE International Conference on Bioinformatics and Biomedicine), pp 4213-4219
- Conference
- 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (Istanbul, Turkey, 05 Dec 2023–08 Dec 2023)
- Publisher
- IEEE
- Number of pages
- 7
- Resource Type
- Conference paper
- Language
- English
- Academic Unit
- College of Medicine; Computer Science (Computing); College of Computing and Informatics; Epidemiology and Biostatistics; SOM Dean - Research Administration; Emergency Medicine
- Scopus ID
- 2-s2.0-85184926137
- Other Identifier
- 9798350337488; 991022154772504721