ArticlePersonal and ubiquitous computing2023
The early warning research on nursing care of stroke patients with intelligent wearable devices under COVID-19.
Article in Personal and ubiquitous computing, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- A Systematic Review of the Application of Artificial Intelligence in Nursing Care: Where are We, and What's Next?Journal of multidisciplinary healthcare · 2024Review
Corrections and comments
- Correction · 2021-03-16Error in Text · Updated to Retraction ·
- Retraction · 2025-11-08Concerns/Issues about Referencing/Attributions · Concerns/Issues about Results and/or Conclusions · Compromised Peer Review · Investigation by Journal/Publisher · Unreliable Results and/or Conclusions · Upgrade/Update of Prior Notice(s) ·
- Erratum issued
- Retracted
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Stroke patients under the background of the new crown epidemic need to be home-based care. However, traditional nursing methods cannot take care of the patients' lives in all aspects. Based on this, based on machine learning algorithms, our work combines regression models and SVM to build a smart wearable device system and builds a system prediction module to predict patient care needs. The node is used to collect human body motion and physiological parameter information and transmit data wirelessly. The software is used to quickly process and analyze the various motion and physiological parameters of the patient and save the analysis and processing structure in the database. By comparing the results of nursing intervention experiments, we can see that the smart wearable device designed in this paper has a certain effect in stroke care.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.