This paper is in the following e-collection/theme issue:

Artificial Intelligence (122) AI Data (2) Digital Pain Assessment and Management (152) Machine Learning (1400) Nails and Musculoskeletal (18)

Published on in Vol 2 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/44779, first published .
A Scalable Radiomics- and Natural Language Processing–Based Machine Learning Pipeline to Distinguish Between Painful and Painless Thoracic Spinal Bone Metastases: Retrospective Algorithm Development and Validation Study

A Scalable Radiomics- and Natural Language Processing–Based Machine Learning Pipeline to Distinguish Between Painful and Painless Thoracic Spinal Bone Metastases: Retrospective Algorithm Development and Validation Study

A Scalable Radiomics- and Natural Language Processing–Based Machine Learning Pipeline to Distinguish Between Painful and Painless Thoracic Spinal Bone Metastases: Retrospective Algorithm Development and Validation Study

Authors of this article:

Hossein Naseri1 Author Orcid Image ;   Sonia Skamene2 Author Orcid Image ;   Marwan Tolba2 Author Orcid Image ;   Mame Daro Faye2 Author Orcid Image ;   Paul Ramia2 Author Orcid Image ;   Julia Khriguian2 Author Orcid Image ;   Marc David2 Author Orcid Image ;   John Kildea1 Author Orcid Image
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