Development and validation of a bedside-available machine learning model to predict discrepancies between SaO₂ and SpO₂: Exploring factors related to the discrepancies

• Raito Sato,

  Roles Data curation, Formal analysis, Investigation, Software, Validation, Visualization, Writing – original draft

  Affiliation School of Medical Sciences, University of Fukui, Yoshida, Fukui, Japan

  https://orcid.org/0009-0002-9465-8315

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• Naoki Ito,

  Roles Data curation, Formal analysis, Investigation

  Affiliation Faculty of Medicine, The University of Tokyo, Bunkyo, Tokyo, Japan

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• Sakina Kadomatsu,

  Roles Investigation, Visualization

  Affiliation Faculty of Medicine, International University of Health and Welfare, Narita, Chiba, Japan

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• Norikazu Hanioka,

  Roles Formal analysis, Investigation

  Affiliation School of Medical Sciences, University of Fukui, Yoshida, Fukui, Japan

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• Mikio Nakajima,

  Roles Supervision, Writing – review & editing

  Affiliations Department of Clinical Epidemiology and Health Economics, School of Public Health, The University of Tokyo, Bunkyo, Tokyo, Japan, Emergency and Critical Care Center, Tokyo Metropolitan Hiroo Hospital, Shibuya, Tokyo, Japan

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• Tadahiro Goto

  Roles Conceptualization, Project administration, Supervision, Writing – review & editing

  * E-mail: tag695@mail.harvard.edu

  Affiliations Department of Health Data Science, Graduate School of Data Science, Yokohama City University, Yokohama, Japan, TXP Medical Co. Ltd., Chiyoda, Tokyo, Japan

  https://orcid.org/0000-0002-5880-2968

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