Academic Journal
CAAC: Co-attentive Actionability Classification for Assessing Patient Education Videos.
| Τίτλος: | CAAC: Co-attentive Actionability Classification for Assessing Patient Education Videos. |
|---|---|
| Συγγραφείς: | Pothugunta, Krishna1 (AUTHOR) kpothugu@nd.edu, Liu, Xiao2 (AUTHOR) xiao.liu.10@asu.edu, Susarla, Anjana3 (AUTHOR) asusarla@msu.edu, Padman, Rema4 (AUTHOR) rpadman@andrew.cmu.edu |
| Πηγή: | INFORMS Journal on Computing. Jul/Aug2026, Vol. 38 Issue 4, p1075-1089. 15p. |
| Θεματικοί όροι: | *Transformer models, *Artificial intelligence, Attention, Medical communication |
| Περίληψη: | The use of video-sharing platforms for disseminating healthcare information is rapidly increasing worldwide. However, professionally produced videos from healthcare organizations cover only a small fraction of known health conditions. In contrast, user-generated videos often address a broader range of health topics and offer personal insights into the daily management of complex diseases, although their educational value remains uncertain. With the growing popularity of self-care videos, there is a critical need to automate manual evaluation methods to ensure these videos meet established health education guidelines. In this study, we propose a hierarchical co-attention mechanism within a transformer-based approach to assess the actionability of health-related videos, using criteria from the Patient Education Materials Assessment Tool (PEMAT). Our model leverages co-attention to dynamically align textual and visual features, enabling a more nuanced understanding of multimodal content. Extensive experiments on 1,795 YouTube videos demonstrate the superior performance of our approach, achieving 82.2% AUC and outperforming state-of-the-art video classifiers. This research contributes to the expanding literature on transformer architectures for video classification and extends the application of PEMAT guidelines to a larger data set of health-related content. By automating the evaluation process, our model overcomes the limitations of manual assessments, offering a scalable and efficient solution for ensuring the quality and reliability of healthcare information on digital platforms. History: This paper has been accepted by Kaushik Dutta for the Special Issue on Responsible AI and Data Science for Social Good. Funding: This research was supported in part by the National Library of Medicine [Grant R01LM013443] of the National Institutes of Health. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0493) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2023.0493). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR] |
| Copyright of INFORMS Journal on Computing is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Business Source Index |
| FullText | Text: Availability: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: CAAC: Co-attentive Actionability Classification for Assessing Patient Education Videos. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Pothugunta%2C+Krishna%22">Pothugunta, Krishna</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> kpothugu@nd.edu</i><br /><searchLink fieldCode="AR" term="%22Liu%2C+Xiao%22">Liu, Xiao</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> xiao.liu.10@asu.edu</i><br /><searchLink fieldCode="AR" term="%22Susarla%2C+Anjana%22">Susarla, Anjana</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> asusarla@msu.edu</i><br /><searchLink fieldCode="AR" term="%22Padman%2C+Rema%22">Padman, Rema</searchLink><relatesTo>4</relatesTo> (AUTHOR)<i> rpadman@andrew.cmu.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22INFORMS+Journal+on+Computing%22">INFORMS Journal on Computing</searchLink>. Jul/Aug2026, Vol. 38 Issue 4, p1075-1089. 15p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Transformer+models%22">Transformer models</searchLink><br />*<searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Attention%22">Attention</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+communication%22">Medical communication</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The use of video-sharing platforms for disseminating healthcare information is rapidly increasing worldwide. However, professionally produced videos from healthcare organizations cover only a small fraction of known health conditions. In contrast, user-generated videos often address a broader range of health topics and offer personal insights into the daily management of complex diseases, although their educational value remains uncertain. With the growing popularity of self-care videos, there is a critical need to automate manual evaluation methods to ensure these videos meet established health education guidelines. In this study, we propose a hierarchical co-attention mechanism within a transformer-based approach to assess the actionability of health-related videos, using criteria from the Patient Education Materials Assessment Tool (PEMAT). Our model leverages co-attention to dynamically align textual and visual features, enabling a more nuanced understanding of multimodal content. Extensive experiments on 1,795 YouTube videos demonstrate the superior performance of our approach, achieving 82.2% AUC and outperforming state-of-the-art video classifiers. This research contributes to the expanding literature on transformer architectures for video classification and extends the application of PEMAT guidelines to a larger data set of health-related content. By automating the evaluation process, our model overcomes the limitations of manual assessments, offering a scalable and efficient solution for ensuring the quality and reliability of healthcare information on digital platforms. History: This paper has been accepted by Kaushik Dutta for the Special Issue on Responsible AI and Data Science for Social Good. Funding: This research was supported in part by the National Library of Medicine [Grant R01LM013443] of the National Institutes of Health. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0493) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2023.0493). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of INFORMS Journal on Computing is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1287/ijoc.2023.0493 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 1075 Subjects: – SubjectFull: Transformer models Type: general – SubjectFull: Artificial intelligence Type: general – SubjectFull: Attention Type: general – SubjectFull: Medical communication Type: general Titles: – TitleFull: CAAC: Co-attentive Actionability Classification for Assessing Patient Education Videos. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Pothugunta, Krishna – PersonEntity: Name: NameFull: Liu, Xiao – PersonEntity: Name: NameFull: Susarla, Anjana – PersonEntity: Name: NameFull: Padman, Rema IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul/Aug2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 10919856 Numbering: – Type: volume Value: 38 – Type: issue Value: 4 Titles: – TitleFull: INFORMS Journal on Computing Type: main |
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