Reducing systematic review burden using Deduklick: a novel, automated, reliable, and explainable deduplication algorithm to foster medical research.

Borissov, Nikolay; Haas, Quentin; Minder, Beatrice; Kopp-Heim, Doris; von Gernler, Marc; Janka, Heidrun; Teodoro, Douglas; Amini, Poorya (2022). Reducing systematic review burden using Deduklick: a novel, automated, reliable, and explainable deduplication algorithm to foster medical research. Systematic Reviews, 11(1), p. 172. BioMed Central 10.1186/s13643-022-02045-9

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BACKGROUND

Identifying and removing reference duplicates when conducting systematic reviews (SRs) remain a major, time-consuming issue for authors who manually check for duplicates using built-in features in citation managers. To address issues related to manual deduplication, we developed an automated, efficient, and rapid artificial intelligence-based algorithm named Deduklick. Deduklick combines natural language processing algorithms with a set of rules created by expert information specialists.

METHODS

Deduklick's deduplication uses a multistep algorithm of data normalization, calculates a similarity score, and identifies unique and duplicate references based on metadata fields, such as title, authors, journal, DOI, year, issue, volume, and page number range. We measured and compared Deduklick's capacity to accurately detect duplicates with the information specialists' standard, manual duplicate removal process using EndNote on eight existing heterogeneous datasets. Using a sensitivity analysis, we manually cross-compared the efficiency and noise of both methods.

DISCUSSION

Deduklick achieved average recall of 99.51%, average precision of 100.00%, and average F1 score of 99.75%. In contrast, the manual deduplication process achieved average recall of 88.65%, average precision of 99.95%, and average F1 score of 91.98%. Deduklick achieved equal to higher expert-level performance on duplicate removal. It also preserved high metadata quality and drastically reduced time spent on analysis. Deduklick represents an efficient, transparent, ergonomic, and time-saving solution for identifying and removing duplicates in SRs searches. Deduklick could therefore simplify SRs production and represent important advantages for scientists, including saving time, increasing accuracy, reducing costs, and contributing to quality SRs.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Pre-clinic Human Medicine > CTU Bern
13 Central Units > Administrative Director's Office > University Library of Bern

UniBE Contributor:

Borissov, Nikolay; Haas, Quentin; Minder, Beatrice; Kopp, Doris; von Gernler, Marc Simon; Janka, Heidrun Ilonka and Amini, Poorya

Subjects:

600 Technology > 610 Medicine & health
000 Computer science, knowledge & systems > 020 Library & information sciences

ISSN:

2046-4053

Publisher:

BioMed Central

Funders:

[198] Innosuisse - Swiss Innovation Agency

Language:

English

Submitter:

Pubmed Import

Date Deposited:

19 Aug 2022 11:59

Last Modified:

05 Dec 2022 16:22

Publisher DOI:

10.1186/s13643-022-02045-9

PubMed ID:

35978441

Uncontrolled Keywords:

Artificial intelligence Bibliographic databases Deduplication Duplicate references Risklick Systematic review Systematic review software

BORIS DOI:

10.48350/172204

URI:

https://boris.unibe.ch/id/eprint/172204

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