Example depiction of the autoML process.

<div><p>Given the suboptimal performance of Boolean searching to identify methodologically sound and clinically relevant studies in large bibliographic databases, exploring machine learning (ML) to efficiently classify studies is warranted. To boost the efficiency of a literature surveil...

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Bibliographic Details
Main Author:	Cynthia Lokker (3813832) (author)
Other Authors:	Wael Abdelkader (19727151) (author), Elham Bagheri (12280984) (author), Rick Parrish (19727154) (author), Chris Cotoi (19727157) (author), Tamara Navarro (3417074) (author), Federico Germini (3458576) (author), Lori-Ann Linkins (19727160) (author), R. Brian Haynes (19727163) (author), Lingyang Chu (19727166) (author), Muhammad Afzal (83943) (author), Alfonso Iorio (3215415) (author)
Published:	2024
Subjects:	Medicine Microbiology Cancer Science Policy Infectious Diseases Biological Sciences not elsewhere classified Information Systems not elsewhere classified xlink "> given large bibliographic databases identify methodologically sound exploring machine learning efficiently classify studies clinically relevant studies 45 %, improving 2012 &# 8211 high methodologic quality retrospective validation test 805 articles indexed met appraisal criteria literature surveillance program final selected algorithm automated ml approach maintained high sensitivity clinical research articles gradient boosting machine lightgbm </ p maintained sensitivity literature surveillance retrospective set clinical relevancy high rigor articles tagged articles needed 5253 articles 424 articles work required validated prospectively subsequent dissemination suboptimal performance sampled datasets prospective study manually appraised find one boosting efficiency boolean searching blinded study achieved 57
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Example depiction of the autoML process.

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