BERT based clinical knowledge extraction for biomedical knowledge graph construction and analysis

Background: Knowledge is evolving over time, often as a result of new discoveries or changes in the adopted methods of reasoning. Also, new facts or evidence may become available, leading to new understandings of complex phenomena. This is particularly true in the biomedical field, where scientists...

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Autores principales:	Ayoub Harnoune, Maryem Rhanoui, Mounia Mikram, Siham Yousfi, Zineb Elkaimbillah, Bouchra El Asri
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Knowledge graph Biomedical informatics Clinical data Natural language processing BERT Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/abf6f66866fa40bbafce7a186cf31479
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spelling	oai:doaj.org-article:abf6f66866fa40bbafce7a186cf314792021-11-04T04:43:49ZBERT based clinical knowledge extraction for biomedical knowledge graph construction and analysis2666-990010.1016/j.cmpbup.2021.100042https://doaj.org/article/abf6f66866fa40bbafce7a186cf314792021-01-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2666990021000410https://doaj.org/toc/2666-9900Background: Knowledge is evolving over time, often as a result of new discoveries or changes in the adopted methods of reasoning. Also, new facts or evidence may become available, leading to new understandings of complex phenomena. This is particularly true in the biomedical field, where scientists and physicians are constantly striving to find new methods of diagnosis, treatment and eventually cure. Knowledge Graphs (KGs) offer a real way of organizing and retrieving the massive and growing amount of biomedical knowledge.Objective: We propose an end-to-end approach for knowledge extraction and analysis from biomedical clinical notes using the Bidirectional Encoder Representations from Transformers (BERT) model and Conditional Random Field (CRF) layer.Methods: The approach is based on knowledge graphs, which can effectively process abstract biomedical concepts such as relationships and interactions between medical entities. Besides offering an intuitive way to visualize these concepts, KGs can solve more complex knowledge retrieval problems by simplifying them into simpler representations or by transforming the problems into representations from different perspectives. We created a biomedical Knowledge Graph using using Natural Language Processing models for named entity recognition and relation extraction. The generated biomedical knowledge graphs (KGs) are then used for question answering.Results: The proposed framework can successfully extract relevant structured information with high accuracy (90.7% for Named-entity recognition (NER), 88% for relation extraction (RE)), according to experimental findings based on real-world 505 patient biomedical unstructured clinical notes.Conclusions:In this paper, we propose a novel end-to-end system for the construction of a biomedical knowledge graph from clinical textual using a variation of BERT models.Ayoub HarnouneMaryem RhanouiMounia MikramSiham YousfiZineb ElkaimbillahBouchra El AsriElsevierarticleKnowledge graphBiomedical informaticsClinical dataNatural language processingBERTComputer applications to medicine. Medical informaticsR858-859.7ENComputer Methods and Programs in Biomedicine Update, Vol 1, Iss , Pp 100042- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Knowledge graph Biomedical informatics Clinical data Natural language processing BERT Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Knowledge graph Biomedical informatics Clinical data Natural language processing BERT Computer applications to medicine. Medical informatics R858-859.7 Ayoub Harnoune Maryem Rhanoui Mounia Mikram Siham Yousfi Zineb Elkaimbillah Bouchra El Asri BERT based clinical knowledge extraction for biomedical knowledge graph construction and analysis
description	Background: Knowledge is evolving over time, often as a result of new discoveries or changes in the adopted methods of reasoning. Also, new facts or evidence may become available, leading to new understandings of complex phenomena. This is particularly true in the biomedical field, where scientists and physicians are constantly striving to find new methods of diagnosis, treatment and eventually cure. Knowledge Graphs (KGs) offer a real way of organizing and retrieving the massive and growing amount of biomedical knowledge.Objective: We propose an end-to-end approach for knowledge extraction and analysis from biomedical clinical notes using the Bidirectional Encoder Representations from Transformers (BERT) model and Conditional Random Field (CRF) layer.Methods: The approach is based on knowledge graphs, which can effectively process abstract biomedical concepts such as relationships and interactions between medical entities. Besides offering an intuitive way to visualize these concepts, KGs can solve more complex knowledge retrieval problems by simplifying them into simpler representations or by transforming the problems into representations from different perspectives. We created a biomedical Knowledge Graph using using Natural Language Processing models for named entity recognition and relation extraction. The generated biomedical knowledge graphs (KGs) are then used for question answering.Results: The proposed framework can successfully extract relevant structured information with high accuracy (90.7% for Named-entity recognition (NER), 88% for relation extraction (RE)), according to experimental findings based on real-world 505 patient biomedical unstructured clinical notes.Conclusions:In this paper, we propose a novel end-to-end system for the construction of a biomedical knowledge graph from clinical textual using a variation of BERT models.
format	article
author	Ayoub Harnoune Maryem Rhanoui Mounia Mikram Siham Yousfi Zineb Elkaimbillah Bouchra El Asri
author_facet	Ayoub Harnoune Maryem Rhanoui Mounia Mikram Siham Yousfi Zineb Elkaimbillah Bouchra El Asri
author_sort	Ayoub Harnoune
title	BERT based clinical knowledge extraction for biomedical knowledge graph construction and analysis
title_short	BERT based clinical knowledge extraction for biomedical knowledge graph construction and analysis
title_full	BERT based clinical knowledge extraction for biomedical knowledge graph construction and analysis
title_fullStr	BERT based clinical knowledge extraction for biomedical knowledge graph construction and analysis
title_full_unstemmed	BERT based clinical knowledge extraction for biomedical knowledge graph construction and analysis
title_sort	bert based clinical knowledge extraction for biomedical knowledge graph construction and analysis
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/abf6f66866fa40bbafce7a186cf31479
work_keys_str_mv	AT ayoubharnoune bertbasedclinicalknowledgeextractionforbiomedicalknowledgegraphconstructionandanalysis AT maryemrhanoui bertbasedclinicalknowledgeextractionforbiomedicalknowledgegraphconstructionandanalysis AT mouniamikram bertbasedclinicalknowledgeextractionforbiomedicalknowledgegraphconstructionandanalysis AT sihamyousfi bertbasedclinicalknowledgeextractionforbiomedicalknowledgegraphconstructionandanalysis AT zinebelkaimbillah bertbasedclinicalknowledgeextractionforbiomedicalknowledgegraphconstructionandanalysis AT bouchraelasri bertbasedclinicalknowledgeextractionforbiomedicalknowledgegraphconstructionandanalysis
_version_	1718445233154293760

BERT based clinical knowledge extraction for biomedical knowledge graph construction and analysis

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