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

Foundations of AI (57) Artificial Intelligence (2129)

Published on in Vol 3 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/55820, first published .
Mitigating Sociodemographic Bias in Opioid Use Disorder Prediction: Fairness-Aware Machine Learning Framework

Mitigating Sociodemographic Bias in Opioid Use Disorder Prediction: Fairness-Aware Machine Learning Framework

Mitigating Sociodemographic Bias in Opioid Use Disorder Prediction: Fairness-Aware Machine Learning Framework

Authors of this article:

Mohammad Yaseliani1 Author Orcid Image ;   Md Noor-E-Alam2, 3 Author Orcid Image ;   Md Mahmudul Hasan1, 4 Author Orcid Image
Article Authors Cited by (5) Tweetations (1) Metrics

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  1. Chisini L, Araújo C, Delpino F, Figueiredo L, Filho A, Schuch H, Nunes B, Demarco F. Dental services use prediction among adults in Southern Brazil: A gender and racial fairness-oriented machine learning approach. Journal of Dentistry 2025;161:105929 View
  2. Feng C, Deng F, Disis M, Gao N, Zhang L. Towards machine learning fairness in classifying multicategory causes of deaths in colorectal or lung cancer patients. Briefings in Bioinformatics 2025;26(4) View
  3. Allen A, Linde-Krieger L, Deschenes J, Mallahan S, Harris A, Felix M, Chalke A, Anderson A, Sharma P, King K, Grant M, Baurley J, Rankin L, Tecot S. Compliance and Satisfaction With a Protocol for Identifying Novel Targets to Support Postpartum Opioid Use Disorder Recovery: Prospective Cohort Study. JMIR Formative Research 2025;9:e77899 View

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  1. Endla P, Patel R, M J, V G, J J, D G. 2025 5th International Conference on Expert Clouds and Applications (ICOECA). Robust Methodology Design to Predict Opioid Overdose System based on AI Assisted Deep Learning Principles View