Clinical-decision support based on medical literature: A complex network approach.
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| Title: | Clinical-decision support based on medical literature: A complex network approach. |
|---|---|
| Authors: | Jiang, Jingchi1, Zheng, Jichuan2, Zhao, Chao1, Su, Jia1, Guan, Yi1 guanyi@hit.edu.cn, Yu, Qiubin3 |
| Source: | Physica A. Oct2016, Vol. 459, p42-54. 13p. |
| Subjects: | Decision support systems, Graph theory, Medical literature, Text Retrieval Conference, Ranking (Statistics) |
| Abstract: | In making clinical decisions, clinicians often review medical literature to ensure the reliability of diagnosis, test, and treatment because the medical literature can answer clinical questions and assist clinicians making clinical decisions. Therefore, finding the appropriate literature is a critical problem for clinical-decision support (CDS). First, the present study employs search engines to retrieve relevant literature about patient records. However, the result of the traditional method is usually unsatisfactory. To improve the relevance of the retrieval result, a medical literature network (MLN) based on these retrieved papers is constructed. Then, we show that this MLN has small-world and scale-free properties of a complex network. According to the structural characteristics of the MLN, we adopt two methods to further identify the potential relevant literature in addition to the retrieved literature. By integrating these potential papers into the MLN, a more comprehensive MLN is built to answer the question of actual patient records. Furthermore, we propose a re-ranking model to sort all papers by relevance. We experimentally find that the re-ranking model can improve the normalized discounted cumulative gain of the results. As participants of the Text Retrieval Conference 2015, our clinical-decision method based on the MLN also yields higher scores than the medians in most topics and achieves the best scores for topics: #11 and #12. These research results indicate that our study can be used to effectively assist clinicians in making clinical decisions, and the MLN can facilitate the investigation of CDS. [ABSTRACT FROM AUTHOR] |
| Copyright of Physica A is the property of Elsevier B.V. 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.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 115799111 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Clinical-decision support based on medical literature: A complex network approach. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Jiang%2C+Jingchi%22">Jiang, Jingchi</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Zheng%2C+Jichuan%22">Zheng, Jichuan</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Zhao%2C+Chao%22">Zhao, Chao</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Su%2C+Jia%22">Su, Jia</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Guan%2C+Yi%22">Guan, Yi</searchLink><relatesTo>1</relatesTo><i> guanyi@hit.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Yu%2C+Qiubin%22">Yu, Qiubin</searchLink><relatesTo>3</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Physica+A%22">Physica A</searchLink>. Oct2016, Vol. 459, p42-54. 13p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Decision+support+systems%22">Decision support systems</searchLink><br /><searchLink fieldCode="DE" term="%22Graph+theory%22">Graph theory</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+literature%22">Medical literature</searchLink><br /><searchLink fieldCode="DE" term="%22Text+Retrieval+Conference%22">Text Retrieval Conference</searchLink><br /><searchLink fieldCode="DE" term="%22Ranking+%28Statistics%29%22">Ranking (Statistics)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In making clinical decisions, clinicians often review medical literature to ensure the reliability of diagnosis, test, and treatment because the medical literature can answer clinical questions and assist clinicians making clinical decisions. Therefore, finding the appropriate literature is a critical problem for clinical-decision support (CDS). First, the present study employs search engines to retrieve relevant literature about patient records. However, the result of the traditional method is usually unsatisfactory. To improve the relevance of the retrieval result, a medical literature network (MLN) based on these retrieved papers is constructed. Then, we show that this MLN has small-world and scale-free properties of a complex network. According to the structural characteristics of the MLN, we adopt two methods to further identify the potential relevant literature in addition to the retrieved literature. By integrating these potential papers into the MLN, a more comprehensive MLN is built to answer the question of actual patient records. Furthermore, we propose a re-ranking model to sort all papers by relevance. We experimentally find that the re-ranking model can improve the normalized discounted cumulative gain of the results. As participants of the Text Retrieval Conference 2015, our clinical-decision method based on the MLN also yields higher scores than the medians in most topics and achieves the best scores for topics: #11 and #12. These research results indicate that our study can be used to effectively assist clinicians in making clinical decisions, and the MLN can facilitate the investigation of CDS. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Physica A is the property of Elsevier B.V. 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.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=115799111 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.physa.2016.04.026 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 42 Subjects: – SubjectFull: Decision support systems Type: general – SubjectFull: Graph theory Type: general – SubjectFull: Medical literature Type: general – SubjectFull: Text Retrieval Conference Type: general – SubjectFull: Ranking (Statistics) Type: general Titles: – TitleFull: Clinical-decision support based on medical literature: A complex network approach. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Jiang, Jingchi – PersonEntity: Name: NameFull: Zheng, Jichuan – PersonEntity: Name: NameFull: Zhao, Chao – PersonEntity: Name: NameFull: Su, Jia – PersonEntity: Name: NameFull: Guan, Yi – PersonEntity: Name: NameFull: Yu, Qiubin IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Text: Oct2016 Type: published Y: 2016 Identifiers: – Type: issn-print Value: 03784371 Numbering: – Type: volume Value: 459 Titles: – TitleFull: Physica A Type: main |
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