Example depiction of the autoML process.

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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