IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0351468.html

Explainable machine learning for predicting activities of daily living at discharge in stroke patients: A retrospective study using SHAP interpretability

Author

Listed:
  • Qian Ye
  • Guilin Fang
  • Liping Li
  • Qinggui Li
  • Yun Yang
  • Lingling Liu

Abstract

Purpose: We aimed to develop a machine learning model to predict activities of daily living (ADL) at discharge in stroke patients and identify key predictors to guide rehabilitation decisions. Materials and methods: Data of 589 stroke inpatients (2019–2024) were split into good (BI ≥ 60) and poor (BI

Suggested Citation

  • Qian Ye & Guilin Fang & Liping Li & Qinggui Li & Yun Yang & Lingling Liu, 2026. "Explainable machine learning for predicting activities of daily living at discharge in stroke patients: A retrospective study using SHAP interpretability," PLOS ONE, Public Library of Science, vol. 21(7), pages 1-13, July.
    • Handle: RePEc:plo:pone00:0351468
    DOI: 10.1371/journal.pone.0351468
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0351468
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0351468&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0351468?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Statistics

    Access and download statistics

    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:plo:pone00:0351468. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.