Adverse drug event detection using natural language processing: A scoping review of supervised learning methods

To reduce adverse drug events (ADEs), hospitals need a system to support them in monitoring ADE occurrence routinely, rapidly, and at scale. Natural language processing (NLP), a computerized approach to analyze text data, has shown promising results for the purpose of ADE detection in the context of...

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Veröffentlicht in:PloS one Jg. 18; H. 1; S. e0279842
Hauptverfasser: Murphy, Rachel M., Klopotowska, Joanna E., de Keizer, Nicolette F., Jager, Kitty J., Leopold, Jan Hendrik, Dongelmans, Dave A., Abu-Hanna, Ameen, Schut, Martijn C.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Public Library of Science 03.01.2023
Public Library of Science (PLoS)
Schlagworte:
Adverse and side effects
Annotations
Artificial intelligence
Automation
Best practice
Biology and Life Sciences
Computational linguistics
Computer and Information Sciences
Computer science
Conditional random fields
Content analysis
Context
Drug-Related Side Effects and Adverse Reactions
Drugs
Electronic Health Records
Electronic medical records
Evaluation
Hospitalization
Hospitals
Humans
Information sources
Language
Language processing
Long short-term memory
Medicine and Health Sciences
Monitoring
Narratives
Natural language interfaces
Natural Language Processing
Patient safety
Performance prediction
Pharmacovigilance
Primary care
Qualitative analysis
Social Sciences
Supervised learning
Supervised Machine Learning
Netherlands
ISSN:1932-6203, 1932-6203
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Abstract To reduce adverse drug events (ADEs), hospitals need a system to support them in monitoring ADE occurrence routinely, rapidly, and at scale. Natural language processing (NLP), a computerized approach to analyze text data, has shown promising results for the purpose of ADE detection in the context of pharmacovigilance. However, a detailed qualitative assessment and critical appraisal of NLP methods for ADE detection in the context of ADE monitoring in hospitals is lacking. Therefore, we have conducted a scoping review to close this knowledge gap, and to provide directions for future research and practice. We included articles where NLP was applied to detect ADEs in clinical narratives within electronic health records of inpatients. Quantitative and qualitative data items relating to NLP methods were extracted and critically appraised. Out of 1,065 articles screened for eligibility, 29 articles met the inclusion criteria. Most frequent tasks included named entity recognition (n = 17; 58.6%) and relation extraction/classification (n = 15; 51.7%). Clinical involvement was reported in nine studies (31%). Multiple NLP modelling approaches seem suitable, with Long Short Term Memory and Conditional Random Field methods most commonly used. Although reported overall performance of the systems was high, it provides an inflated impression given a steep drop in performance when predicting the ADE entity or ADE relation class. When annotating corpora, treating an ADE as a relation between a drug and non-drug entity seems the best practice. Future research should focus on semi-automated methods to reduce the manual annotation effort, and examine implementation of the NLP methods in practice.
AbstractList To reduce adverse drug events (ADEs), hospitals need a system to support them in monitoring ADE occurrence routinely, rapidly, and at scale. Natural language processing (NLP), a computerized approach to analyze text data, has shown promising results for the purpose of ADE detection in the context of pharmacovigilance. However, a detailed qualitative assessment and critical appraisal of NLP methods for ADE detection in the context of ADE monitoring in hospitals is lacking. Therefore, we have conducted a scoping review to close this knowledge gap, and to provide directions for future research and practice. We included articles where NLP was applied to detect ADEs in clinical narratives within electronic health records of inpatients. Quantitative and qualitative data items relating to NLP methods were extracted and critically appraised. Out of 1,065 articles screened for eligibility, 29 articles met the inclusion criteria. Most frequent tasks included named entity recognition (n = 17; 58.6%) and relation extraction/classification (n = 15; 51.7%). Clinical involvement was reported in nine studies (31%). Multiple NLP modelling approaches seem suitable, with Long Short Term Memory and Conditional Random Field methods most commonly used. Although reported overall performance of the systems was high, it provides an inflated impression given a steep drop in performance when predicting the ADE entity or ADE relation class. When annotating corpora, treating an ADE as a relation between a drug and non-drug entity seems the best practice. Future research should focus on semi-automated methods to reduce the manual annotation effort, and examine implementation of the NLP methods in practice.
To reduce adverse drug events (ADEs), hospitals need a system to support them in monitoring ADE occurrence routinely, rapidly, and at scale. Natural language processing (NLP), a computerized approach to analyze text data, has shown promising results for the purpose of ADE detection in the context of pharmacovigilance. However, a detailed qualitative assessment and critical appraisal of NLP methods for ADE detection in the context of ADE monitoring in hospitals is lacking. Therefore, we have conducted a scoping review to close this knowledge gap, and to provide directions for future research and practice. We included articles where NLP was applied to detect ADEs in clinical narratives within electronic health records of inpatients. Quantitative and qualitative data items relating to NLP methods were extracted and critically appraised. Out of 1,065 articles screened for eligibility, 29 articles met the inclusion criteria. Most frequent tasks included named entity recognition (n = 17; 58.6%) and relation extraction/classification (n = 15; 51.7%). Clinical involvement was reported in nine studies (31%). Multiple NLP modelling approaches seem suitable, with Long Short Term Memory and Conditional Random Field methods most commonly used. Although reported overall performance of the systems was high, it provides an inflated impression given a steep drop in performance when predicting the ADE entity or ADE relation class. When annotating corpora, treating an ADE as a relation between a drug and non-drug entity seems the best practice. Future research should focus on semi-automated methods to reduce the manual annotation effort, and examine implementation of the NLP methods in practice.To reduce adverse drug events (ADEs), hospitals need a system to support them in monitoring ADE occurrence routinely, rapidly, and at scale. Natural language processing (NLP), a computerized approach to analyze text data, has shown promising results for the purpose of ADE detection in the context of pharmacovigilance. However, a detailed qualitative assessment and critical appraisal of NLP methods for ADE detection in the context of ADE monitoring in hospitals is lacking. Therefore, we have conducted a scoping review to close this knowledge gap, and to provide directions for future research and practice. We included articles where NLP was applied to detect ADEs in clinical narratives within electronic health records of inpatients. Quantitative and qualitative data items relating to NLP methods were extracted and critically appraised. Out of 1,065 articles screened for eligibility, 29 articles met the inclusion criteria. Most frequent tasks included named entity recognition (n = 17; 58.6%) and relation extraction/classification (n = 15; 51.7%). Clinical involvement was reported in nine studies (31%). Multiple NLP modelling approaches seem suitable, with Long Short Term Memory and Conditional Random Field methods most commonly used. Although reported overall performance of the systems was high, it provides an inflated impression given a steep drop in performance when predicting the ADE entity or ADE relation class. When annotating corpora, treating an ADE as a relation between a drug and non-drug entity seems the best practice. Future research should focus on semi-automated methods to reduce the manual annotation effort, and examine implementation of the NLP methods in practice.
Audience Academic
Author Leopold, Jan Hendrik
Jager, Kitty J.
Schut, Martijn C.
Murphy, Rachel M.
Abu-Hanna, Ameen
de Keizer, Nicolette F.
Dongelmans, Dave A.
Klopotowska, Joanna E.
AuthorAffiliation 1 Department of Medical Informatics, Amsterdam UMC (location AMC), Amsterdam, The Netherlands
National University of Science and Technology, PAKISTAN
2 Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
3 Department of Intensive Care Medicine, Amsterdam UMC (location AMC), Amsterdam, The Netherlands
AuthorAffiliation_xml – name: 2 Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
– name: National University of Science and Technology, PAKISTAN
– name: 3 Department of Intensive Care Medicine, Amsterdam UMC (location AMC), Amsterdam, The Netherlands
– name: 1 Department of Medical Informatics, Amsterdam UMC (location AMC), Amsterdam, The Netherlands
Author_xml – sequence: 1
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  orcidid: 0000-0002-2602-5658
  surname: Murphy
  fullname: Murphy, Rachel M.
– sequence: 2
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  surname: Klopotowska
  fullname: Klopotowska, Joanna E.
– sequence: 3
  givenname: Nicolette F.
  surname: de Keizer
  fullname: de Keizer, Nicolette F.
– sequence: 4
  givenname: Kitty J.
  surname: Jager
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  surname: Leopold
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/36595517$$D View this record in MEDLINE/PubMed
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Copyright Copyright: © 2023 Murphy et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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2023 Murphy et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
2023 Murphy et al 2023 Murphy et al
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– notice: 2023 Murphy et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
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License Copyright: © 2023 Murphy et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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SubjectTerms Adverse and side effects
Annotations
Artificial intelligence
Automation
Best practice
Biology and Life Sciences
Computational linguistics
Computer and Information Sciences
Computer science
Conditional random fields
Content analysis
Context
Drug-Related Side Effects and Adverse Reactions
Drugs
Electronic Health Records
Electronic medical records
Evaluation
Hospitalization
Hospitals
Humans
Information sources
Language
Language processing
Long short-term memory
Medicine and Health Sciences
Monitoring
Narratives
Natural language interfaces
Natural Language Processing
Patient safety
Performance prediction
Pharmacovigilance
Primary care
Qualitative analysis
Social Sciences
Supervised learning
Supervised Machine Learning
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Title Adverse drug event detection using natural language processing: A scoping review of supervised learning methods
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