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Elmitwalli S, Mehegan J, Wellock G, Gallagher A, Gilmore A. Topic prediction for tobacco control based on COP9 tweets using machine learning techniques. PLoS One 2024; 19:e0298298. [PMID: 38358979 PMCID: PMC10868820 DOI: 10.1371/journal.pone.0298298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 01/23/2024] [Indexed: 02/17/2024] Open
Abstract
The prediction of tweets associated with specific topics offers the potential to automatically focus on and understand online discussions surrounding these issues. This paper introduces a comprehensive approach that centers on the topic of "harm reduction" within the broader context of tobacco control. The study leveraged tweets from the period surrounding the ninth Conference of the Parties to review the Framework Convention on Tobacco Control (COP9) as a case study to pilot this approach. By using Latent Dirichlet Allocation (LDA)-based topic modeling, the study successfully categorized tweets related to harm reduction. Subsequently, various machine learning techniques were employed to predict these topics, achieving a prediction accuracy of 91.87% using the Random Forest algorithm. Additionally, the study explored correlations between retweets and sentiment scores. It also conducted a toxicity analysis to understand the extent to which online conversations lacked neutrality. Understanding the topics, sentiment, and toxicity of Twitter data is crucial for identifying public opinion and its formation. By specifically focusing on the topic of "harm reduction" in tweets related to COP9, the findings offer valuable insights into online discussions surrounding tobacco control. This understanding can aid policymakers in effectively informing the public and garnering public support, ultimately contributing to the successful implementation of tobacco control policies.
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Affiliation(s)
- Sherif Elmitwalli
- Tobacco Control Research Group, Department for Health, University of Bath, Bath, United Kingdom
| | - John Mehegan
- Tobacco Control Research Group, Department for Health, University of Bath, Bath, United Kingdom
| | - Georgie Wellock
- Tobacco Control Research Group, Department for Health, University of Bath, Bath, United Kingdom
| | - Allen Gallagher
- Tobacco Control Research Group, Department for Health, University of Bath, Bath, United Kingdom
| | - Anna Gilmore
- Tobacco Control Research Group, Department for Health, University of Bath, Bath, United Kingdom
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Ghimire S, Nguyen-Huy T, Prasad R, Deo RC, Casillas-Pérez D, Salcedo-Sanz S, Bhandari B. Hybrid Convolutional Neural Network-Multilayer Perceptron Model for Solar Radiation Prediction. Cognit Comput 2022. [DOI: 10.1007/s12559-022-10070-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Tabashum T, Xiao T, Jayaraman C, Mummidisetty CK, Jayaraman A, Albert MV. Autoencoder Composite Scoring to Evaluate Prosthetic Performance in Individuals with Lower Limb Amputation. Bioengineering (Basel) 2022; 9:bioengineering9100572. [PMID: 36290540 PMCID: PMC9598529 DOI: 10.3390/bioengineering9100572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 10/02/2022] [Accepted: 10/11/2022] [Indexed: 11/21/2022] Open
Abstract
We created an overall assessment metric using a deep learning autoencoder to directly compare clinical outcomes in a comparison of lower limb amputees using two different prosthetic devices—a mechanical knee and a microprocessor-controlled knee. Eight clinical outcomes were distilled into a single metric using a seven-layer deep autoencoder, with the developed metric compared to similar results from principal component analysis (PCA). The proposed methods were used on data collected from ten participants with a dysvascular transfemoral amputation recruited for a prosthetics research study. This single summary metric permitted a cross-validated reconstruction of all eight scores, accounting for 83.29% of the variance. The derived score is also linked to the overall functional ability in this limited trial population, as improvements in each base clinical score led to increases in this developed metric. There was a highly significant increase in this autoencoder-based metric when the subjects used the microprocessor-controlled knee (p < 0.001, repeated measures ANOVA). A traditional PCA metric led to a similar interpretation but captured only 67.3% of the variance. The autoencoder composite score represents a single-valued, succinct summary that can be useful for the holistic assessment of highly variable, individual scores in limited clinical datasets.
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Affiliation(s)
- Thasina Tabashum
- Department of Computer Science and Engineering, University of North Texas, Denton, TX 76203, USA
- Correspondence:
| | - Ting Xiao
- Department of Computer Science and Engineering, University of North Texas, Denton, TX 76203, USA
- Department of Information Science, University of North Texas, Denton, TX 76203, USA
| | - Chandrasekaran Jayaraman
- Max Näder Lab for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, Chicago, IL 60611, USA
- Department of Physical Medicine and Rehabilitation, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Chaithanya K. Mummidisetty
- Max Näder Lab for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, Chicago, IL 60611, USA
| | - Arun Jayaraman
- Max Näder Lab for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, Chicago, IL 60611, USA
- Department of Physical Medicine and Rehabilitation, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Mark V. Albert
- Department of Computer Science and Engineering, University of North Texas, Denton, TX 76203, USA
- Department of Physical Medicine and Rehabilitation, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
- Department of Biomedical Engineering, University of North Texas, Denton, TX 76203, USA
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