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Methods for Analyzing Unknown Health Risk Based on Nature Language Process (NLP).
1Trade union, Guilin Medical University, Guilin, China.
Studies in Health Technology and Informatics
|November 26, 2023
Summary
Natural Language Processing (NLP) aids public health by analyzing epidemic data. However, NLP-interpreted health risks are currently difficult for non-experts to understand, limiting practical application.
Area of Science:
- Public Health
- Computational Linguistics
- Epidemiology
Background:
- Rapid epidemic spread necessitates efficient health risk factor analysis.
- Natural Language Processing (NLP) offers potential for analyzing large-scale health data.
- Existing methods struggle to quickly interpret complex health risks from diverse data sources.
Purpose of the Study:
- To analyze health risk factors using NLP on Chinese internet case reports during the Delta variant outbreak.
- To explore NLP's capability in interpreting health risks for public health decision support.
- To provide methodologies for future responses to emerging health challenges.
Main Methods:
- Utilized NLP techniques to process and analyze official online case reports.
- Focused on data from the period following the Delta variant outbreak in China.
- Interpreted health risks identified through NLP analysis.
Main Results:
- NLP analysis identified key health risks associated with the Delta variant.
- Findings indicated that NLP-interpreted health risks were challenging for non-public health professionals to comprehend.
- The practical application of NLP for direct health threat analysis is currently limited.
Conclusions:
- While NLP enhances efficiency in large-scale data analysis for public health, its direct interpretation of health risks requires further development.
- NLP's primary current value lies in decision support and improving analytical efficiency.
- Future research should focus on making NLP-derived health insights more accessible and practical for public health professionals.
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