An interpretable machine learning framework for opioid overdose surveillance from emergency medical services records

• S. Scott Graham ,

  Contributed equally to this work with: S. Scott Graham, Kasey Claborn

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

  * E-mail: ssg@utexas.edu

  Affiliations Department of Rhetoric & Writing, Center for Health Communication, University of Texas at Austin, Austin, TX, United States of Amedrica, Addiction Research Institute, University of Texas at Austin, Austin, TX, United States of Amedrica

  https://orcid.org/0000-0003-1569-2428

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• Savannah Shifflet ,

  Roles Data curation, Investigation, Writing – original draft, Writing – review & editing

  ¶‡ SS and MA also contributed equally to this work.

  Affiliation Addiction Research Institute, University of Texas at Austin, Austin, TX, United States of Amedrica

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• Maaz Amjad ,

  Roles Methodology, Writing – review & editing

  ¶‡ SS and MA also contributed equally to this work.

  Affiliation Addiction Research Institute, University of Texas at Austin, Austin, TX, United States of Amedrica

  https://orcid.org/0000-0002-5969-9085

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• Kasey Claborn

  Contributed equally to this work with: S. Scott Graham, Kasey Claborn

  Roles Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Writing – review & editing

  Affiliations Addiction Research Institute, University of Texas at Austin, Austin, TX, United States of Amedrica, Steve Hicks School of Social Work, University of Texas at Austin, Austin, TX, United States of Amedrica

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