Academic Journal
Applying natural language processing and machine learning techniques to patient experience feedback: a systematic review
| Τίτλος: | Applying natural language processing and machine learning techniques to patient experience feedback: a systematic review |
|---|---|
| Συγγραφείς: | Khanbhai, M, Anyadi, P, Symons, J, Flott, K, Darzi, A, Mayer, E |
| Συνεισφορές: | Imperial College Healthcare NHS Trust- BRC Funding, National Institute for Health Research, The Health Foundation, Imperial College Healthcare NHS Trust, NHS England |
| Στοιχεία εκδότη: | BMJ Publishing Group |
| Έτος έκδοσης: | 2021 |
| Συλλογή: | Imperial College London: Spiral |
| Θεματικοί όροι: | BMJ health informatics, computer methodologies, information management, patient care |
| Περιγραφή: | Objectives 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. |
| Τύπος εγγράφου: | article in journal/newspaper |
| Γλώσσα: | unknown |
| Relation: | BMJ Health & Care Informatics; http://hdl.handle.net/10044/1/86838; RDB04; n/a; RDB18 79650; FR775; WSSY_P85168; RDF01; 452981 |
| DOI: | 10.1136/bmjhci-2020-100262 |
| Διαθεσιμότητα: | http://hdl.handle.net/10044/1/86838 https://doi.org/10.1136/bmjhci-2020-100262 |
| Rights: | © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. http://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/. ; http://creativecommons.org/licenses/by-nc/4.0/ |
| Αριθμός Καταχώρησης: | edsbas.276ED822 |
| Βάση Δεδομένων: | BASE |
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| Items | – Name: Title Label: Title Group: Ti Data: Applying natural language processing and machine learning techniques to patient experience feedback: a systematic review – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Khanbhai%2C+M%22">Khanbhai, M</searchLink><br /><searchLink fieldCode="AR" term="%22Anyadi%2C+P%22">Anyadi, P</searchLink><br /><searchLink fieldCode="AR" term="%22Symons%2C+J%22">Symons, J</searchLink><br /><searchLink fieldCode="AR" term="%22Flott%2C+K%22">Flott, K</searchLink><br /><searchLink fieldCode="AR" term="%22Darzi%2C+A%22">Darzi, A</searchLink><br /><searchLink fieldCode="AR" term="%22Mayer%2C+E%22">Mayer, E</searchLink> – Name: Author Label: Contributors Group: Au Data: Imperial College Healthcare NHS Trust- BRC Funding<br />National Institute for Health Research<br />The Health Foundation<br />Imperial College Healthcare NHS Trust<br />NHS England – Name: Publisher Label: Publisher Information Group: PubInfo Data: BMJ Publishing Group – Name: DatePubCY Label: Publication Year Group: Date Data: 2021 – Name: Subset Label: Collection Group: HoldingsInfo Data: Imperial College London: Spiral – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22BMJ+health+informatics%22">BMJ health informatics</searchLink><br /><searchLink fieldCode="DE" term="%22computer+methodologies%22">computer methodologies</searchLink><br /><searchLink fieldCode="DE" term="%22information+management%22">information management</searchLink><br /><searchLink fieldCode="DE" term="%22patient+care%22">patient care</searchLink> – Name: Abstract Label: Description Group: Ab Data: Objectives 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. – Name: TypeDocument Label: Document Type Group: TypDoc Data: article in journal/newspaper – Name: Language Label: Language Group: Lang Data: unknown – Name: NoteTitleSource Label: Relation Group: SrcInfo Data: BMJ Health & Care Informatics; http://hdl.handle.net/10044/1/86838; RDB04; n/a; RDB18 79650; FR775; WSSY_P85168; RDF01; 452981 – Name: DOI Label: DOI Group: ID Data: 10.1136/bmjhci-2020-100262 – Name: URL Label: Availability Group: URL Data: http://hdl.handle.net/10044/1/86838<br />https://doi.org/10.1136/bmjhci-2020-100262 – Name: Copyright Label: Rights Group: Cpyrght Data: © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. http://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/. ; http://creativecommons.org/licenses/by-nc/4.0/ – Name: AN Label: Accession Number Group: ID Data: edsbas.276ED822 |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1136/bmjhci-2020-100262 Languages: – Text: unknown Subjects: – SubjectFull: BMJ health informatics Type: general – SubjectFull: computer methodologies Type: general – SubjectFull: information management Type: general – SubjectFull: patient care Type: general Titles: – TitleFull: Applying natural language processing and machine learning techniques to patient experience feedback: a systematic review Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Khanbhai, M – PersonEntity: Name: NameFull: Anyadi, P – PersonEntity: Name: NameFull: Symons, J – PersonEntity: Name: NameFull: Flott, K – PersonEntity: Name: NameFull: Darzi, A – PersonEntity: Name: NameFull: Mayer, E – PersonEntity: Name: NameFull: Imperial College Healthcare NHS Trust- BRC Funding – PersonEntity: Name: NameFull: National Institute for Health Research – PersonEntity: Name: NameFull: The Health Foundation – PersonEntity: Name: NameFull: Imperial College Healthcare NHS Trust – PersonEntity: Name: NameFull: NHS England IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2021 Identifiers: – Type: issn-locals Value: edsbas – Type: issn-locals Value: edsbas.oa |
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