Machine learning decision support model for discharge planning in stroke patients.

Journal: Journal of clinical nursing
Published Date: Feb 15, 2024

Abstract

BACKGROUND/AIM: Efficient discharge for stroke patients is crucial but challenging. The study aimed to develop early predictive models to explore which patient characteristics and variables significantly influence the discharge planning of patients, based on the data available within 24 h of admission.

Authors

  • Yanli Cui
    Department of Neurology, Nanfang Hospital, Southern Medical University, Guangzhou, China.
  • Lijun Xiang
    Department of Neurology, Nanfang Hospital, Southern Medical University, Guangzhou, China.
  • Peng Zhao
    Department of Neurosurgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.
  • Jian Chen
    School of Pharmacy, Shanghai Jiaotong University, Shanghai, China.
  • Lei Cheng
    State Key Laboratory of Oral Diseases, Sichuan University, Chengdu, China.
  • Lin Liao
    Department of Neurology, Nanfang Hospital, Southern Medical University, Guangzhou, China.
  • Mingyu Yan
    Department of Neurology, Nanfang Hospital, Southern Medical University, Guangzhou, China.
  • Xiaomei Zhang
    School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China.

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