IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i6p983-d774482.html
   My bibliography  Save this article

Automatic Classification of National Health Service Feedback

Author

Listed:
  • Christopher Haynes

    (School of Engineering, Computing and Mathematics, University of Plymouth, Plymouth PL4 8AA, UK)

  • Marco A. Palomino

    (School of Engineering, Computing and Mathematics, University of Plymouth, Plymouth PL4 8AA, UK)

  • Liz Stuart

    (School of Engineering, Computing and Mathematics, University of Plymouth, Plymouth PL4 8AA, UK)

  • David Viira

    (Faculty of Health, University Hospitals Plymouth, Derriford Rd., Plymouth PL6 8DH, UK)

  • Frances Hannon

    (Faculty of Health, University Hospitals Plymouth, Derriford Rd., Plymouth PL6 8DH, UK)

  • Gemma Crossingham

    (Faculty of Health, University Hospitals Plymouth, Derriford Rd., Plymouth PL6 8DH, UK)

  • Kate Tantam

    (Faculty of Health, University Hospitals Plymouth, Derriford Rd., Plymouth PL6 8DH, UK)

Abstract

Text datasets come in an abundance of shapes, sizes and styles. However, determining what factors limit classification accuracy remains a difficult task which is still the subject of intensive research. Using a challenging UK National Health Service (NHS) dataset, which contains many characteristics known to increase the complexity of classification, we propose an innovative classification pipeline. This pipeline switches between different text pre-processing, scoring and classification techniques during execution. Using this flexible pipeline, a high level of accuracy has been achieved in the classification of a range of datasets, attaining a micro-averaged F1 score of 93.30% on the Reuters-21578 “ApteMod” corpus. An evaluation of this flexible pipeline was carried out using a variety of complex datasets compared against an unsupervised clustering approach. The paper describes how classification accuracy is impacted by an unbalanced category distribution, the rare use of generic terms and the subjective nature of manual human classification.

Suggested Citation

  • Christopher Haynes & Marco A. Palomino & Liz Stuart & David Viira & Frances Hannon & Gemma Crossingham & Kate Tantam, 2022. "Automatic Classification of National Health Service Feedback," Mathematics, MDPI, vol. 10(6), pages 1-23, March.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:6:p:983-:d:774482
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/6/983/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/6/983/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Matthias Schonlau & Nick Guenther & Ilia Sucholutsky, 2017. "Text mining with n-gram variables," Stata Journal, StataCorp LP, vol. 17(4), pages 866-881, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Florentina Hristea & Cornelia Caragea, 2022. "Preface to the Special Issue “Natural Language Processing (NLP) and Machine Learning (ML)—Theory and Applications”," Mathematics, MDPI, vol. 10(14), pages 1-5, July.
    2. Bogdan Oancea, 2023. "Automatic Product Classification Using Supervised Machine Learning Algorithms in Price Statistics," Mathematics, MDPI, vol. 11(7), pages 1-32, March.
    3. Sergio Bolívar & Alicia Nieto-Reyes & Heather L. Rogers, 2022. "Supervised Classification of Healthcare Text Data Based on Context-Defined Categories," Mathematics, MDPI, vol. 10(12), pages 1-31, June.
    4. Sergio Bolívar & Alicia Nieto-Reyes & Heather L. Rogers, 2023. "Statistical Depth for Text Data: An Application to the Classification of Healthcare Data," Mathematics, MDPI, vol. 11(1), pages 1-20, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Cristina Cattaneo & Daniela Gireco & Nicola Lacetera & Mario Macis, 2024. "Out-Group Penalties in Refugee Assistance: A Survey Experiment," CESifo Working Paper Series 10950, CESifo.
    2. Muhammed-Fatih Kaya, 2022. "Pattern Labelling of Business Communication Data," Group Decision and Negotiation, Springer, vol. 31(6), pages 1203-1234, December.
    3. Paul M. Anglin & Yanmin Gao, 2023. "Value of Communication and Social Media: An Equilibrium Theory of Messaging," The Journal of Real Estate Finance and Economics, Springer, vol. 66(4), pages 861-903, May.
    4. Martin Brown & Jan Schmitz & Christian Zehnder, 2023. "Communication and Hidden Action: A Credit Market Experiment," Working Papers 23.02, Swiss National Bank, Study Center Gerzensee.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:10:y:2022:i:6:p:983-:d:774482. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.