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BMC Medical Informatics and Decision Making
Published in: BMC Medical Informatics and Decision Making 1/2023

Open Access 01-12-2023 | COVID-19 | Research

Entity and relation extraction from clinical case reports of COVID-19: a natural language processing approach

Authors: Shaina Raza, Brian Schwartz

Published in: BMC Medical Informatics and Decision Making | Issue 1/2023

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Abstract

Background

Extracting 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.

Objective

This 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.

Methods

The 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.

Results

The 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.

Conclusions

A 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.
Appendix
Available only for authorised users
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Metadata
Title
Entity and relation extraction from clinical case reports of COVID-19: a natural language processing approach
Authors
Shaina Raza
Brian Schwartz
Publication date
01-12-2023
Publisher
BioMed Central
Keyword
COVID-19
Published in
BMC Medical Informatics and Decision Making / Issue 1/2023
Electronic ISSN: 1472-6947
DOI
https://doi.org/10.1186/s12911-023-02117-3

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