Characteristics of the dataset, preprocessing, and feature extraction steps employed by AutoML in the training of the model selected from each dataset experiment<sup>b'*'</sup>.

<p>Characteristics of the dataset, preprocessing, and feature extraction steps employed by AutoML in the training of the model selected from each dataset experiment<sup>b'<a href="/article/info:doi/10.1371/journal.pdig.0000299#t002fn002" target="_blank">*&l...

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التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Cynthia Lokker (3813832) (author)
مؤلفون آخرون:	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)
منشور في:	2024
الموضوعات:	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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Characteristics of the dataset, preprocessing, and feature extraction steps employed by AutoML in the training of the model selected from each dataset experiment<sup>b'*'</sup>.

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