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

Qian Ye, Guilin Fang, Liping Li, Qinggui Li, Yun Yang and Lingling Liu

PLOS ONE, 2026, vol. 21, issue 7, 1-13

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

Date: 2026
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0351468 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 51468&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0351468

DOI: 10.1371/journal.pone.0351468

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().