Predicting social isolation in maintenance hemodialysis patients using machine learning methods
A cross-sectional study
Bibliographic Data
| ID | 15525554 |
|---|---|
| Authors | Ying Li (0000-0003-0678-9535, Jishou University, corresponding author), Y Y Li (0000-0003-4140-504X, Jishou University), Wen Zhao (0000-0001-6649-3926, Qilu University of Technology), Wenwen Zhao (0000-0002-7438-6300, School of Nursing, Qilu Medical University), Boyang Wang (0000-0001-5767-6077, Shanghai East Hospital) |
| Year | 2026 |
| Volume | 17 |
| Pages | 1776298-1776298 |
| Publication date | 2026-02-18 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Psychiatry (JOURNAL) |
| Journal identifiers | ISSN: 1664-0640 • E-ISSN: 1664-0640 |
| Publisher | Frontiers Media (PUBLISHER • CH) |
| DOI | 10.3389/fpsyt.2026.1776298 |
| PMID | 41788655 |
| OpenAlex | W7130359432 |
| Language | EN |
| References cited | 54 |
The prediction model based on RF has a good effect in identifying the social isolation risk of MHD patients. These findings enable clinicians to stratify high-risk populations and implement timely and targeted intervention measures, effectively reducing the risk of adverse consequences. Future multicenter studies should validate these results in larger cohorts
Decision tree · Gradient boosting · Hemodialysis · Intervention (counseling · Logistic regression · Random forest · Social isolation · Support vector machine · Chronic Disease Management Strategies · Dialysis and Renal Disease Management · Heart Failure Treatment and Management
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| Citation velocity | historical |
|---|---|
| Highly cited | No |