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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
PubMed

Insights

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.

Keywords:
Contextual searchData analysisPatient classification system

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