ArticleComputational intelligence and neuroscience2022
Classification and Pathological Diagnosis of Idiopathic Interstitial Pneumonia.
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 It has been retracted, and should not be counted.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, 3 citations in OpenAlex.
- Retracted: Classification and Pathological Diagnosis of Idiopathic Interstitial Pneumonia.Computational intelligence and neuroscience · 2023Article
Corrections and comments
- Retraction · 2023-07-12Concerns/Issues about Data · Concerns/Issues about Results and/or Conclusions · Concerns/Issues about Referencing/Attributions · Concerns/Issues about Peer Review · Informed/Patient Consent - None/Withdrawn · Investigation by Journal/Publisher · Investigation by Third Party · Lack of IRB/IACUC Approval and/or Compliance · Computer-Aided Content or Computer-Generated Content · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Idiopathic interstitial pneumonia (IIP) is a group of progressive lower respiratory tract diseases of unknown origin characterized by diffuse alveolitis and alveolar structural disorders leading to pulmonary fibrillation and hypertension, pulmonary heart disease, and right heart failure due to pulmonary fibrosis, and more than half of them die from respiratory failure. To address these problems of overly complex prediction methods and large data sets involved in the prediction process of interstitial pneumonia, this paper proposes a prediction model for interstitial pneumonia which is based on the Gaussian Parsimonious Bayes algorithm. Three usual tests of pneumonia, specifically from various patients, were collected as the sample set. These samples are divided into training and testing sets. Additionally, a cross-validation strategy was used to avoid the overfitting problem. The results showed that the prediction model based on the Gaussian Parsimonious Bayes algorithm predicted 92% accuracy on the test set, and the Parsimonious Bayes method could directly predict the final detection of interstitial pneumonia based on the usual pneumonia test pneumonia. In addition, it was found that the closer the data distribution of the sample set was to a normal distribution, the higher the prediction accuracy was, and then, after excluding pneumonia from the test below 60 points, the prediction accuracy reached 96%.
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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.