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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Vydáno v:	PloS one Ročník 18; číslo 1; s. e0279842
Hlavní autoři:	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.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Public Library of Science 03.01.2023 Public Library of Science (PLoS)
Témata:	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
On-line přístup:	Získat plný text
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Popis
Shrnutí:	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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 content type line 14 ObjectType-Literature Review-2 ObjectType-Feature-3 ObjectType-Article-2 content type line 23 ObjectType-Review-1 Competing Interests: The authors declare that they have no conflict of interest.
ISSN:	1932-6203 1932-6203
DOI:	10.1371/journal.pone.0279842