ArticleBMC medical informatics and decision making2023
Entity and relation extraction from clinical case reports of COVID-19: a natural language processing approach.
Article in BMC medical informatics and decision making, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Structuring medication safety narratives: development and evaluation of the medication-related incident reports annotation scheme.Frontiers in digital health · 2026Article
- Improving few-shot named entity recognition for large language models using structured dynamic prompting with retrieval augmented generation.NPJ artificial intelligence · 2026Article
- Predicting 30-Day Postoperative Mortality and American Society of Anesthesiologists Physical Status Using Retrieval-Augmented Large Language Models: Development and Validation Study.Journal of medical Internet research · 2025Article
- Exploring COVID-related relationship extraction: Contrasting data sources and analyzing misinformation.Heliyon · 2024Article
- Applied artificial intelligence in dentistry: emerging data modalities and modeling approaches.Frontiers in artificial intelligence · 2024Review
- A framework for multi-faceted content analysis of social media chatter regarding non-medical use of prescription medications.BMC digital health · 2023Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
backgroundExtracting relevant information about infectious diseases is an essential task. However, a significant obstacle in supporting public health research is the lack of methods for effectively mining large amounts of health data.
objectiveThis study aims to use natural language processing (NLP) to extract the key information (clinical factors, social determinants of health) from published cases in the literature.
methodsThe proposed framework integrates a data layer for preparing a data cohort from clinical case reports; an NLP layer to find the clinical and demographic-named entities and relations in the texts; and an evaluation layer for benchmarking performance and analysis. The focus of this study is to extract valuable information from COVID-19 case reports.
resultsThe named entity recognition implementation in the NLP layer achieves a performance gain of about 1-3% compared to benchmark methods. Furthermore, even without extensive data labeling, the relation extraction method outperforms benchmark methods in terms of accuracy (by 1-8% better). A thorough examination reveals the disease's presence and symptoms prevalence in patients.
conclusionsA similar approach can be generalized to other infectious diseases. It is worthwhile to use prior knowledge acquired through transfer learning when researching other infectious diseases.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.