Development of a pharmaceutical science systematic review process using a semi-automated machine learning tool: Intravenous drug compatibility in the neonatal intensive care setting

被引：1

作者：

De Silva, D. Thisuri N. ^{[1
]}

Moore, Brioni R. ^{[1
,2
,3
,4
]}

Strunk, Tobias ^{[3
,4
,5
]}

Petrovski, Michael ^{[6
]}

Varis, Vanessa ^{[7
]}

Chai, Kevin ^{[8
]}

Ng, Leo ^{[9
,10
]}

Batty, Kevin T. ^{[1
,2
]}

机构：

[1] Curtin Univ, Curtin Med Sch, GPO Box U1987, Perth, WA 6845, Australia

[2] Curtin Univ, Curtin Hlth Innovat Res Inst, Perth, WA, Australia

[3] Univ Western Australia, Med Sch, Crawley, WA, Australia

[4] Telethon Kids Inst, Wesfarmers Ctr Vaccines & Infect Dis, Nedlands, WA, Australia

[5] King Edward Mem Hosp, Child & Adolescent Hlth Serv, Neonatal Directorate, Subiaco, WA, Australia

[6] King Edward Mem Hosp, Women & Newborn Hlth Serv, Pharm Dept, Subiaco, WA, Australia

[7] Curtin Univ, Univ Lib, Perth, WA, Australia

[8] Curtin Univ, Sch Populat Hlth, Perth, WA, Australia

[9] Curtin Univ, Curtin Sch Allied Hlth, Perth, WA, Australia

[10] Swinburne Univ Technol, Sch Hlth Sci, Hawthorn, Vic, Australia

来源：

PHARMACOLOGY RESEARCH & PERSPECTIVES | 2024年 / 12卷 / 01期

关键词：

machine learning; pharmaceutical science; physicochemical compatibility; systematic review; PICO;

D O I：

10.1002/prp2.1170

中图分类号：

R9 [药学];

学科分类号：

1007 ;

摘要：

Our objective was to establish and test a machine learning-based screening process that would be applicable to systematic reviews in pharmaceutical sciences. We used the SPIDER (Sample, Phenomenon of Interest, Design, Evaluation, Research type) model, a broad search strategy, and a machine learning tool (Research Screener) to identify relevant references related to y-site compatibility of 95 intravenous drugs used in neonatal intensive care settings. Two independent reviewers conducted pilot studies, including manual screening and evaluation of Research Screener, and used the kappa-coefficient for inter-reviewer reliability. After initial deduplication of the search strategy results, 27 597 references were available for screening. Research Screener excluded 1735 references, including 451 duplicate titles and 1269 reports with no abstract/title, which were manually screened. The remainder (25 862) were subject to the machine learning screening process. All eligible articles for the systematic review were extracted from <10% of the references available for screening. Moderate inter-reviewer reliability was achieved, with kappa-coefficient >= 0.75. Overall, 324 references were subject to full-text reading and 118 were deemed relevant for the systematic review. Our study showed that a broad search strategy to optimize the literature captured for systematic reviews can be efficiently screened by the semi-automated machine learning tool, Research Screener.

引用

页数：8