Machine Learning in Discharge Planning for Stroke Patients: A Review of Feature Representation and Aggregation Strategies With Suggestions for Improvement...Cui Y, Xiang L, Zhao P, et al. Machine learning decision support model for discharge planning in stroke patients. Journal of Clinical Nursing (John Wiley & Sons, Inc). 2024;33(8):3145-3160.
The article focuses on a machine learning (ML) decision support model for discharge planning in acute stroke patients, highlighting its integration of clinical severity, functional status, and social determinants of health to enhance nursing-led decision-making. The study employs a prospective cohor...
| Publicado en: | Journal of Clinical Nursing (John Wiley & Sons, Inc.) Vol. 35; no. 9; pp. 3989 - 3991 |
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| Autores principales: | , |
| Formato: | commentary letter Journal Article |
| Publicado: |
John Wiley & Sons, Inc.
Sep2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=195863193&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 195863193 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09621067 LVDZ jtl: Journal of Clinical Nursing (John Wiley & Sons, Inc.) issn: 09621067 maglogo: N pubinfo: dt: Sep2026 vid: 35 iid: 9 pid: 52269 pub: John Wiley & Sons, Inc. artinfo: ui: 195863193 192078930 195863193 195863193 10.1111/jocn.70285 195863193 ppf: 3989 ppct: 2 formats: tig: atl: Machine Learning in Discharge Planning for Stroke Patients: A Review of Feature Representation and Aggregation Strategies With Suggestions for Improvement...Cui Y, Xiang L, Zhao P, et al. Machine learning decision support model for discharge planning in stroke patients. Journal of Clinical Nursing (John Wiley & Sons, Inc). 2024;33(8):3145-3160. aug: au: Liu, Siyu Cai, Jiaxin affil: School of Computer and Information Engineering, Xiamen University of Technology, Xiamen Fujian,, China sug: subj: Machine Learning Methods Decision Support Systems, Clinical Utilization Patient Discharge Standards Stroke Patients Discharge Planning Nursing Informatics Stroke Rehabilitation Reproducibility of Results ab: The article focuses on a machine learning (ML) decision support model for discharge planning in acute stroke patients, highlighting its integration of clinical severity, functional status, and social determinants of health to enhance nursing-led decision-making. The study employs a prospective cohort design and identifies a random forest model as the best-performing algorithm, with key predictors including the NIH Stroke Scale (NIHSS) score, Barthel Index, FRAIL score, and family income. The authors discuss methodological considerations, suggesting that current ML approaches treating variables independently may overlook complex interactions, and propose advanced feature aggregation techniques like spectral methods to improve model robustness and interpretability. Emphasizing that ML tools should augment rather than replace clinical judgment, the article underscores the potential for improved transferability and clinical relevance through refined representation learning in discharge planning systems. pubtype: Academic Journal doctype: commentary letter Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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