ArticleContrast media & molecular imaging2022
Risk Prediction Method of Obstetric Nursing Based on Data Mining.
Article in Contrast media & molecular imaging, 2022. 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 It has been retracted, and should not be counted.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.
- Retracted: Risk Prediction Method of Obstetric Nursing Based on Data Mining.Contrast media & molecular imaging · 2023Article
Corrections and comments
- Retraction · 2023-10-04Concerns/Issues about Data · Concerns/Issues about Results and/or Conclusions · Concerns/Issues about Referencing/Attributions · Concerns/Issues about Peer Review · Investigation by Journal/Publisher · Investigation by Third Party · Paper Mill · Computer-Aided Content or Computer-Generated Content · Unreliable Results and/or Conclusions · · See also: https://pubpeer.com/publications/743205243BF9B23FBCF1E2448BBE6F
- Retracted
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Obstetric nursing is not only complex but also prone to risks, which can have adverse effects on hospitals. Improper handling of existing risks in obstetric care can lead to enormous harm to patients and families. Therefore, it is necessary to pay attention to the risks of obstetric nursing, especially to predict the risks in a timely manner, and take effective measures to prevent them in time, so as to achieve the purpose of allowing patients to recover as soon as possible. Data mining has powerful forecasting function, so this paper proposes to combine the data-mining-based support vector machine method and XGBoost method into a forecasting model, which overcomes the shortcomings of unstable forecasting and low accuracy of a single forecasting model. The experimental results of this paper have shown that the prediction accuracy of the SVM-XGBoost combined prediction model has reached 100%, the accuracy of the single SVM prediction model is about 78%, and the accuracy of the single XGBoost prediction model is about 75%. Compared with the single SVM model and the XGBoost prediction model, the accuracy rate had increased by about 22% and 25%, and the precision rate and recall rate are also improved. Therefore, it is very suitable to use the SVM-XGBoost combined prediction model to predict the risk of obstetric nursing.
Indexed as
Identifiers
What Socratic 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 Socratic graph.