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

Development and Evaluation of Research Methods, Instruments and Tools (630) Machine Learning (1719) Digital Pain Assessment and Management (195) Electronic Health Records (1173)

Published on in Vol 6, No 6 (2022): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/36998, first published .
Improving Pain Assessment Using Vital Signs and Pain Medication for Patients With Sickle Cell Disease: Retrospective Study

Improving Pain Assessment Using Vital Signs and Pain Medication for Patients With Sickle Cell Disease: Retrospective Study

Improving Pain Assessment Using Vital Signs and Pain Medication for Patients With Sickle Cell Disease: Retrospective Study

Authors of this article:

Swati Padhee1 Author Orcid Image ;   Gary K Nave Jr2 Author Orcid Image ;   Tanvi Banerjee1 Author Orcid Image ;   Daniel M Abrams2 Author Orcid Image ;   Nirmish Shah3 Author Orcid Image
Article Authors Cited by (3) Tweetations (6) Metrics

Journals

  1. Vuong C, Utkarsh K, Stojancic R, Subramaniam A, Fernandez O, Banerjee T, Abrams D, Fijnvandraat K, Shah N. Use of consumer wearables to monitor and predict pain in patients with sickle cell disease. Frontiers in Digital Health 2023;5 View
  2. Machado T, Neto F, Gonçalves M, Barbosa C, Barreto M, Sadeghi A. Exploring machine learning algorithms in sickle cell disease patient data: A systematic review. PLOS ONE 2024;19(11):e0313315 View

Conference Proceedings

  1. Agarwal A, Banerjee T, Romine W, Cajita M. 2024 IEEE International Conference on Big Data (BigData). Debias-CLR: A Contrastive Learning Based Debiasing Method for Algorithmic Fairness in Healthcare Applications View