Natural Language Processing in Electronic Health Records in relation to healthcare decision-making: A systematic review.

Hossain, E; Rana, R; Higgins, N; Soar, J; Barua, PD; Pisani, AR; Turner, K

Natural Language Processing in Electronic Health Records in relation to healthcare decision-making: A systematic review.

Hossain, E Rana, R Higgins, N Soar, J Barua, PD Pisani, AR Turner, K

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Comput Biol Med, 2023, 155, pp. 106649
Issue Date:: 2023-03

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Field	Value	Language
dc.contributor.author	Hossain, E
dc.contributor.author	Rana, R
dc.contributor.author	Higgins, N
dc.contributor.author	Soar, J
dc.contributor.author	Barua, PD
dc.contributor.author	Pisani, AR
dc.contributor.author	Turner, K
dc.date.accessioned	2024-04-05T01:30:21Z
dc.date.available	2023-02-07
dc.date.available	2024-04-05T01:30:21Z
dc.date.issued	2023-03
dc.identifier.citation	Comput Biol Med, 2023, 155, pp. 106649
dc.identifier.issn	0010-4825
dc.identifier.issn	1879-0534
dc.identifier.uri	http://hdl.handle.net/10453/177491
dc.description.abstract	BACKGROUND: Natural Language Processing (NLP) is widely used to extract clinical insights from Electronic Health Records (EHRs). However, the lack of annotated data, automated tools, and other challenges hinder the full utilisation of NLP for EHRs. Various Machine Learning (ML), Deep Learning (DL) and NLP techniques are studied and compared to understand the limitations and opportunities in this space comprehensively. METHODOLOGY: After screening 261 articles from 11 databases, we included 127 papers for full-text review covering seven categories of articles: (1) medical note classification, (2) clinical entity recognition, (3) text summarisation, (4) deep learning (DL) and transfer learning architecture, (5) information extraction, (6) Medical language translation and (7) other NLP applications. This study follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. RESULT AND DISCUSSION: EHR was the most commonly used data type among the selected articles, and the datasets were primarily unstructured. Various ML and DL methods were used, with prediction or classification being the most common application of ML or DL. The most common use cases were: the International Classification of Diseases, Ninth Revision (ICD-9) classification, clinical note analysis, and named entity recognition (NER) for clinical descriptions and research on psychiatric disorders. CONCLUSION: We find that the adopted ML models were not adequately assessed. In addition, the data imbalance problem is quite important, yet we must find techniques to address this underlining problem. Future studies should address key limitations in studies, primarily identifying Lupus Nephritis, Suicide Attempts, perinatal self-harmed and ICD-9 classification.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	Elsevier
dc.relation.ispartof	Comput Biol Med
dc.relation.isbasedon	10.1016/j.compbiomed.2023.106649
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering, 11 Medical and Health Sciences
dc.subject.classification	Biomedical Engineering
dc.subject.classification	3102 Bioinformatics and computational biology
dc.subject.classification	4203 Health services and systems
dc.subject.classification	4601 Applied computing
dc.subject.mesh	Humans
dc.subject.mesh	Natural Language Processing
dc.subject.mesh	Electronic Health Records
dc.subject.mesh	Machine Learning
dc.subject.mesh	Information Storage and Retrieval
dc.subject.mesh	Delivery of Health Care
dc.subject.mesh	Humans
dc.subject.mesh	Natural Language Processing
dc.subject.mesh	Information Storage and Retrieval
dc.subject.mesh	Delivery of Health Care
dc.subject.mesh	Electronic Health Records
dc.subject.mesh	Machine Learning
dc.subject.mesh	Humans
dc.subject.mesh	Natural Language Processing
dc.subject.mesh	Electronic Health Records
dc.subject.mesh	Machine Learning
dc.subject.mesh	Information Storage and Retrieval
dc.subject.mesh	Delivery of Health Care
dc.title	Natural Language Processing in Electronic Health Records in relation to healthcare decision-making: A systematic review.
dc.type	Journal Article
utslib.citation.volume	155
utslib.location.activity	United States
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
utslib.for	11 Medical and Health Sciences
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2024-04-05T01:30:19Z
pubs.publication-status	Published
pubs.volume	155

Abstract:

BACKGROUND: Natural Language Processing (NLP) is widely used to extract clinical insights from Electronic Health Records (EHRs). However, the lack of annotated data, automated tools, and other challenges hinder the full utilisation of NLP for EHRs. Various Machine Learning (ML), Deep Learning (DL) and NLP techniques are studied and compared to understand the limitations and opportunities in this space comprehensively. METHODOLOGY: After screening 261 articles from 11 databases, we included 127 papers for full-text review covering seven categories of articles: (1) medical note classification, (2) clinical entity recognition, (3) text summarisation, (4) deep learning (DL) and transfer learning architecture, (5) information extraction, (6) Medical language translation and (7) other NLP applications. This study follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. RESULT AND DISCUSSION: EHR was the most commonly used data type among the selected articles, and the datasets were primarily unstructured. Various ML and DL methods were used, with prediction or classification being the most common application of ML or DL. The most common use cases were: the International Classification of Diseases, Ninth Revision (ICD-9) classification, clinical note analysis, and named entity recognition (NER) for clinical descriptions and research on psychiatric disorders. CONCLUSION: We find that the adopted ML models were not adequately assessed. In addition, the data imbalance problem is quite important, yet we must find techniques to address this underlining problem. Future studies should address key limitations in studies, primarily identifying Lupus Nephritis, Suicide Attempts, perinatal self-harmed and ICD-9 classification.

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http://hdl.handle.net/10453/177491