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Targeting COVID-19 and Human Resources for Health News Information Extraction: Algorithm Development and Validation
Mathieu Ravaut1, Ruochen Zhao1, Duy Phung1
1Nanyang Technological University, Singapore, Singapore.
JMIR AI
|October 30, 2024
Summary
This study demonstrates that combining natural language processing (NLP) with human analysis effectively processes vast news data on the COVID-19 pandemic and its health workforce impacts, enabling timely insights for policy.
Area of Science:
- Computational linguistics
- Public health informatics
- Health services research
Background:
- Global pandemics like COVID-19 generate overwhelming volumes of online news.
- Analyzing this information is crucial for understanding events and guiding policy.
- Human capacity is insufficient to process this data deluge effectively.
Purpose of the Study:
- To explore natural language processing (NLP) for rapid analysis of high-volume news.
- To develop a human-computer symbiosis workflow for health workforce insights.
- To support strategic policy dialogue, advocacy, and decision-making.
Main Methods:
- Reviewed 2.8 million COVID-19 health workforce news articles (Jan 2020-June 2022) from WHO EIOS.
- Utilized NLP models (classification, extractive summarization) and human analysis.
- Developed the DeepCovid system trained on diverse global sources.
Main Results:
- Rule-based classification refined data to 8508 relevant articles.
- DeepCovid achieved 98.98% ROC-AUC for classification.
- Extractive summarization achieved a mean ROUGE score of 47.76.
Conclusions:
- Synergizing NLP models and human analysis is feasible for health workforce intelligence.
- The DeepCovid approach offers an agile, timely global perspective.
- This system complements scientific literature with open-source intelligence.
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