ArticleComputational intelligence and neuroscience2022
Detection of Types of Mental Illness through the Social Network Using Ensembled Deep Learning Model.
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 It has been retracted, and should not be counted.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 2 papers.
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
2 citing papers in PubMed, 20 citations in OpenAlex.
- The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century.Bioengineering (Basel, Switzerland) · 2024Review
- Retracted: Detection of Types of Mental Illness through the Social Network Using Ensembled Deep Learning Model.Computational intelligence and neuroscience · 2023Article
Corrections and comments
- Retraction · 2023-10-11Concerns/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 ·
- Retracted
Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In today's era, social networking platforms are widely used to share emotions. These types of emotions are often analyzed to predict the user's behavior. In this paper, these types of sentiments are classified to predict the mental illness of the user using the ensembled deep learning model. The Reddit social networking platform is used for the analysis, and the ensembling deep learning model is implemented through convolutional neural network and the recurrent neural network. In this work, multiclass classification is performed for predicting mental illness such as anxiety vs. nonanxiety, bipolar vs. nonbipolar, dementia vs. nondementia, and psychotic vs. nonpsychotic. The performance parameters used for evaluating the models are accuracy, precision, recall, and F1 score. The proposed ensemble model used for performing the multiclass classification has performed better than the other models, with an accuracy greater than 92% in predicting the class.
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.