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Entity and relation extraction from clinical case reports of COVID-19: a natural language processing approach
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 (...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9879259/ https://www.ncbi.nlm.nih.gov/pubmed/36703154 http://dx.doi.org/10.1186/s12911-023-02117-3 |
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author | Raza, Shaina Schwartz, Brian |
author_facet | Raza, Shaina Schwartz, Brian |
author_sort | Raza, Shaina |
collection | PubMed |
description | 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12911-023-02117-3. |
format | Online Article Text |
id | pubmed-9879259 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-98792592023-01-27 Entity and relation extraction from clinical case reports of COVID-19: a natural language processing approach Raza, Shaina Schwartz, Brian BMC Med Inform Decis Mak Research 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12911-023-02117-3. BioMed Central 2023-01-26 /pmc/articles/PMC9879259/ /pubmed/36703154 http://dx.doi.org/10.1186/s12911-023-02117-3 Text en © Crown 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Raza, Shaina Schwartz, Brian Entity and relation extraction from clinical case reports of COVID-19: a natural language processing approach |
title | Entity and relation extraction from clinical case reports of COVID-19: a natural language processing approach |
title_full | Entity and relation extraction from clinical case reports of COVID-19: a natural language processing approach |
title_fullStr | Entity and relation extraction from clinical case reports of COVID-19: a natural language processing approach |
title_full_unstemmed | Entity and relation extraction from clinical case reports of COVID-19: a natural language processing approach |
title_short | Entity and relation extraction from clinical case reports of COVID-19: a natural language processing approach |
title_sort | entity and relation extraction from clinical case reports of covid-19: a natural language processing approach |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9879259/ https://www.ncbi.nlm.nih.gov/pubmed/36703154 http://dx.doi.org/10.1186/s12911-023-02117-3 |
work_keys_str_mv | AT razashaina entityandrelationextractionfromclinicalcasereportsofcovid19anaturallanguageprocessingapproach AT schwartzbrian entityandrelationextractionfromclinicalcasereportsofcovid19anaturallanguageprocessingapproach |