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Data Analysis of COVID-19 Hospital Records Using Contextual Patient Classification System
Vrushabh Gada1, Madhura Shegaonkar1, Madhura Inamdar1
1K. J. Somaiya College of Engineering, Mumbai, Maharashtra 400077 India.
Annals of Data Science
|April 16, 2024
Summary
A new contextual patient classification system achieved 97.4% accuracy in analyzing Coronavirus Disease 2019 (COVID-19) data. This system aids in better preparedness for future pandemic waves by analyzing patient data and outcomes.
Area of Science:
- Medical Informatics
- Computational Biology
- Epidemiology
Background:
- The Coronavirus Disease 2019 (COVID-19) pandemic has severely impacted global health systems.
- Despite medical advancements, rapid virus spread necessitates improved data analysis for patient management.
Purpose of the Study:
- To develop and evaluate a contextual patient classification system for analyzing COVID-19 patient data.
- To analyze COVID-19 and non-COVID-19 patient data, including treatments, symptoms, and demographics.
Main Methods:
- Utilized the Knuth-Morris-Pratt algorithm for contextual patient classification.
- Analyzed discharge summary data from a research hospital.
- Examined factors such as medications, medical services, tests, pulse, temperature, age, and gender.
Main Results:
- Achieved a classification accuracy of 97.4% for the contextual patient classification system.
- Studied the death versus survival ratio for COVID-19 positive patients.
- Analyzed the impact of various factors on patient outcomes.
Conclusions:
- The developed system offers a robust method for classifying patients during pandemics.
- Combining data analysis with contextual classification enhances preparedness for future health crises like COVID-19.
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