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[Evolution analysis of diagnosis and treatment plans of corona virus disease 2019 based on text mining]

Chonghui Guo1, Liangchen Xu1, Wei Wei2

  • 1Institute of Systems Engineering, Dalian University of Technology, Dalian, Liaoning 116024, P.R.China.

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi = Journal of Biomedical Engineering = Shengwu Yixue Gongchengxue Zazhi
|April 29, 2021
PubMed

Insights

This study analyzes the evolution of China's COVID-19 diagnosis and treatment plans. Text mining reveals key changes, offering insights for clinical practice and future guideline development.

Area of Science:

  • Public Health
  • Infectious Diseases
  • Medical Informatics

Background:

  • The COVID-19 pandemic necessitated rapid development and adaptation of clinical guidelines.
  • Understanding the evolution of these guidelines is crucial for effective public health response.

Purpose of the Study:

  • To analyze the evolution of diagnosis and treatment plans for COVID-19.
  • To provide a reference for clinical practice and future guideline formulation.

Main Methods:

  • Comparative and visual analysis of nine COVID-19 diagnosis and treatment plans.
  • Text mining to assess text similarity and identify evolution patterns.
  • Analysis of plans issued by China's National Health Commission from January 26, 2020, to August 19, 2020.

Main Results:

  • Quantified text similarity between different versions of the COVID-19 guidelines.
  • Identified key trends and changes in diagnosis and treatment strategies over time.
  • Visualized the evolution of the plans to highlight significant shifts.

Conclusions:

  • The study provides a clear understanding of the evolutionary trajectory of COVID-19 diagnosis and treatment plans.
  • Findings offer valuable insights for healthcare professionals and policymakers in refining clinical guidelines.
  • The methodology can be applied to analyze the evolution of other public health emergency response plans.

Keywords:
corona virus disease 2019diagnosis and treatment planevolution analysistext mining

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