ArticleComputational intelligence and neuroscience2022
Visualization Method of Key Knowledge Points of Nursing Teaching Management System Based on SOM Algorithm and Biomedical Diagnosis.
Article in Computational intelligence and neuroscience, 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 It has been retracted, and should not be counted.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: Visualization Method of Key Knowledge Points of Nursing Teaching Management System Based on SOM Algorithm and Biomedical Diagnosis.Computational intelligence and neuroscience · 2023Article
Corrections and comments
- Retraction · 2023-08-09Concerns/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/100F4810C72C9132A902824BF5398F
- Retracted
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The traditional nursing teaching knowledge point recommendation algorithm based on collaborative filtering is difficult to deal with the problem of data sparsity, while the traditional recommendation algorithm based on matrix decomposition has poor scalability in dealing with high-dimensional data, and their recommendation results are only determined according to the prediction score, resulting in low recommendation accuracy. In view of this, a nursing teaching knowledge point recommendation method based on a SOM neural network and ranking factor decomposition machine is proposed. Firstly, the SOM neural network is used to cluster users based on users' academic background information, then the partial order relationship of nursing teaching knowledge points is constructed by using users' explicit and implicit web access behavior, and finally, the factor decomposition machine is used as the ranking function to classify users' academic background web access behavior, borrowing nursing teaching introduction text, and other characteristic information were modeled, and the peer-to-peer ranking learning algorithm was used to accurately recommend nursing teaching knowledge points. Experimental results show that the proposed method can effectively alleviate the problem of data sparsity and improve the accuracy and efficiency of recommendations.
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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.