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dc.contributor.authorKhanbhai, M
dc.contributor.authorAnyadi, P
dc.contributor.authorSymons, J
dc.contributor.authorFlott, K
dc.contributor.authorDarzi, A
dc.contributor.authorMayer, E
dc.date.accessioned2021-05-25T11:45:29Z
dc.date.available2021-05-25T11:45:29Z
dc.identifier.citationBMJ health & care informatics, 2021, 28 (1)
dc.identifier.issn2632-1009
dc.identifier.urihttps://repository.icr.ac.uk/handle/internal/4577
dc.identifier.eissn2632-1009
dc.identifier.eissn2632-1009en_US
dc.identifier.doi10.1136/bmjhci-2020-100262
dc.identifier.doi10.1136/bmjhci-2020-100262en_US
dc.description.abstractObjectives Unstructured free-text patient feedback contains rich information, and analysing these data manually would require a lot of personnel resources which are not available in most healthcare organisations.To undertake a systematic review of the literature on the use of natural language processing (NLP) and machine learning (ML) to process and analyse free-text patient experience data.Methods Databases were systematically searched to identify articles published between January 2000 and December 2019 examining NLP to analyse free-text patient feedback. Due to the heterogeneous nature of the studies, a narrative synthesis was deemed most appropriate. Data related to the study purpose, corpus, methodology, performance metrics and indicators of quality were recorded.Results Nineteen articles were included. The majority (80%) of studies applied language analysis techniques on patient feedback from social media sites (unsolicited) followed by structured surveys (solicited). Supervised learning was frequently used (n=9), followed by unsupervised (n=6) and semisupervised (n=3). Comments extracted from social media were analysed using an unsupervised approach, and free-text comments held within structured surveys were analysed using a supervised approach. Reported performance metrics included the precision, recall and F-measure, with support vector machine and Naïve Bayes being the best performing ML classifiers.Conclusion NLP and ML have emerged as an important tool for processing unstructured free text. Both supervised and unsupervised approaches have their role depending on the data source. With the advancement of data analysis tools, these techniques may be useful to healthcare organisations to generate insight from the volumes of unstructured free-text data.
dc.formatPrint
dc.languageeng
dc.language.isoeng
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0
dc.titleApplying natural language processing and machine learning techniques to patient experience feedback: a systematic review.
dc.typeJournal Article
dcterms.dateAccepted2021-01-12
rioxxterms.versionVoR
rioxxterms.versionofrecord10.1136/bmjhci-2020-100262
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by-nc/4.0
rioxxterms.typeJournal Article/Review
dc.relation.isPartOfBMJ health & care informatics
pubs.issue1
pubs.notesNot known
pubs.organisational-group/ICR
pubs.organisational-group/ICR
pubs.publication-statusPublished
pubs.volume28en_US
pubs.embargo.termsNot known
dc.contributor.icrauthorDarzi, Araen


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