A decision support system for automating document retrieval and citation screening
<p dir="ltr">The systematic literature review (SLR) process includes several steps to collect secondary data and analyze it to answer research questions. In this context, the document retrieval and primary study selection steps are heavily intertwined and known for their repetitivene...
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2021
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| _version_ | 1864513545459204096 |
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| author | Raymon van Dinter (10521952) |
| author2 | Cagatay Catal (6897842) Bedir Tekinerdogan (6897839) |
| author2_role | author author |
| author_facet | Raymon van Dinter (10521952) Cagatay Catal (6897842) Bedir Tekinerdogan (6897839) |
| author_role | author |
| dc.creator.none.fl_str_mv | Raymon van Dinter (10521952) Cagatay Catal (6897842) Bedir Tekinerdogan (6897839) |
| dc.date.none.fl_str_mv | 2021-11-15T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.eswa.2021.115261 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/A_decision_support_system_for_automating_document_retrieval_and_citation_screening/24459055 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Artificial intelligence Computer vision and multimedia computation Machine learning Language, communication and culture Linguistics Systematic literature review (SLR) Citation screening Document retrieval Decision support Automation Deep learning Convolutional neural network Natural language processing |
| dc.title.none.fl_str_mv | A decision support system for automating document retrieval and citation screening |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">The systematic literature review (SLR) process includes several steps to collect secondary data and analyze it to answer research questions. In this context, the document retrieval and primary study selection steps are heavily intertwined and known for their repetitiveness, high human workload, and difficulty identifying all relevant literature. This study aims to reduce human workload and error of the document retrieval and primary study selection processes using a decision support system (DSS). An open-source DSS is proposed that supports the document retrieval step, dataset preprocessing, and citation classification. The DSS is domain-independent, as it has proven to carefully select an article’s relevance based solely on the title and abstract. These features can be consistently retrieved from scientific database APIs. Additionally, the DSS is designed to run in the cloud without any required programming knowledge for reviewers. A Multi-Channel CNN architecture is implemented to support the citation screening process. With the provided DSS, reviewers can fill in their search strategy and manually label only a subset of the citations. The remaining unlabeled citations are automatically classified and sorted based on probability. It was shown that for four out of five review datasets, the DSS's use achieved significant workload savings of at least 10%. The cross-validation results show that the system provides consistent results up to 88.3% of work saved during citation screening. In two cases, our model yielded a better performance over the benchmark review datasets. As such, the proposed approach can assist the development of systematic literature reviews independent of the domain. The proposed DSS is effective and can substantially decrease the document retrieval and citation screening steps' workload and error rate.</p><h2>Other Information</h2><p dir="ltr">Published in: Expert Systems with Applications<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.eswa.2021.115261" target="_blank">https://dx.doi.org/10.1016/j.eswa.2021.115261</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_4dd93b03068aae8690af26d1fd60f59b |
| identifier_str_mv | 10.1016/j.eswa.2021.115261 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24459055 |
| publishDate | 2021 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | A decision support system for automating document retrieval and citation screeningRaymon van Dinter (10521952)Cagatay Catal (6897842)Bedir Tekinerdogan (6897839)Information and computing sciencesArtificial intelligenceComputer vision and multimedia computationMachine learningLanguage, communication and cultureLinguisticsSystematic literature review (SLR)Citation screeningDocument retrievalDecision supportAutomationDeep learningConvolutional neural networkNatural language processing<p dir="ltr">The systematic literature review (SLR) process includes several steps to collect secondary data and analyze it to answer research questions. In this context, the document retrieval and primary study selection steps are heavily intertwined and known for their repetitiveness, high human workload, and difficulty identifying all relevant literature. This study aims to reduce human workload and error of the document retrieval and primary study selection processes using a decision support system (DSS). An open-source DSS is proposed that supports the document retrieval step, dataset preprocessing, and citation classification. The DSS is domain-independent, as it has proven to carefully select an article’s relevance based solely on the title and abstract. These features can be consistently retrieved from scientific database APIs. Additionally, the DSS is designed to run in the cloud without any required programming knowledge for reviewers. A Multi-Channel CNN architecture is implemented to support the citation screening process. With the provided DSS, reviewers can fill in their search strategy and manually label only a subset of the citations. The remaining unlabeled citations are automatically classified and sorted based on probability. It was shown that for four out of five review datasets, the DSS's use achieved significant workload savings of at least 10%. The cross-validation results show that the system provides consistent results up to 88.3% of work saved during citation screening. In two cases, our model yielded a better performance over the benchmark review datasets. As such, the proposed approach can assist the development of systematic literature reviews independent of the domain. The proposed DSS is effective and can substantially decrease the document retrieval and citation screening steps' workload and error rate.</p><h2>Other Information</h2><p dir="ltr">Published in: Expert Systems with Applications<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.eswa.2021.115261" target="_blank">https://dx.doi.org/10.1016/j.eswa.2021.115261</a></p>2021-11-15T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.eswa.2021.115261https://figshare.com/articles/journal_contribution/A_decision_support_system_for_automating_document_retrieval_and_citation_screening/24459055CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/244590552021-11-15T00:00:00Z |
| spellingShingle | A decision support system for automating document retrieval and citation screening Raymon van Dinter (10521952) Information and computing sciences Artificial intelligence Computer vision and multimedia computation Machine learning Language, communication and culture Linguistics Systematic literature review (SLR) Citation screening Document retrieval Decision support Automation Deep learning Convolutional neural network Natural language processing |
| status_str | publishedVersion |
| title | A decision support system for automating document retrieval and citation screening |
| title_full | A decision support system for automating document retrieval and citation screening |
| title_fullStr | A decision support system for automating document retrieval and citation screening |
| title_full_unstemmed | A decision support system for automating document retrieval and citation screening |
| title_short | A decision support system for automating document retrieval and citation screening |
| title_sort | A decision support system for automating document retrieval and citation screening |
| topic | Information and computing sciences Artificial intelligence Computer vision and multimedia computation Machine learning Language, communication and culture Linguistics Systematic literature review (SLR) Citation screening Document retrieval Decision support Automation Deep learning Convolutional neural network Natural language processing |