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Open Access

Peer-reviewed

Research Article

Justifying model complexity: Evaluating transfer learning against classical models for intraoperative nociception monitoring under anesthesia

Chanseo Lee ,

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: chanseo.lee@yale.edu

Affiliations Yale School of Medicine, New Haven, Connecticut, United States of America, Sporo Health, Boston, Massachusetts, United States of America

https://orcid.org/0000-0001-6157-0881

⨯
Jaihyoung Lee,

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing

Affiliations Yale School of Medicine, New Haven, Connecticut, United States of America, Sporo Health, Boston, Massachusetts, United States of America
⨯
Kimon-Aristotelis Vogt,

Roles Data curation, Formal analysis, Investigation, Methodology, Software, Writing – original draft, Writing – review & editing

Affiliations Sporo Health, Boston, Massachusetts, United States of America, Paulson School of Engineering and Applied Sciences, Harvard University, Boston, Massachusetts, United States of America
⨯
Muhammad Munshi

Roles Conceptualization, Formal analysis, Methodology, Validation, Visualization, Writing – original draft, Writing – review & editing

Affiliation Yale School of Medicine, New Haven, Connecticut, United States of America
⨯

Justifying model complexity: Evaluating transfer learning against classical models for intraoperative nociception monitoring under anesthesia

Chanseo Lee,
Jaihyoung Lee,
Kimon-Aristotelis Vogt,
Muhammad Munshi

x

Published: February 17, 2026
https://doi.org/10.1371/journal.pone.0342688

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Nociception
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Deep learning
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Surgical and invasive medical procedures
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Trees
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