Using machine learning to predict risk of incident opioid use disorder among fee-for-service Medicare beneficiaries: A prognostic study

OBJECTIVE: To develop and validate a machine-learning algorithm to improve prediction of incident OUD diagnosis among Medicare beneficiaries with ≥1 opioid prescriptions. METHODS: This prognostic study included 361,527 fee-for-service Medicare beneficiaries, without cancer, filling ≥1 opioid prescri...

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Detalles Bibliográficos
Autores principales: Lo-Ciganic, Wei-Hsuan, Huang, James L., Zhang, Hao H., Weiss, Jeremy C., Kwoh, C. Kent, Donohue, Julie M., Gordon, Adam J., Cochran, Gerald, Malone, Daniel C., Kuza, Courtney C., Gellad, Walid F.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7367453/
https://www.ncbi.nlm.nih.gov/pubmed/32678860
http://dx.doi.org/10.1371/journal.pone.0235981

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7367453/
https://www.ncbi.nlm.nih.gov/pubmed/32678860
http://dx.doi.org/10.1371/journal.pone.0235981

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