Cargando…
Integrating human services and criminal justice data with claims data to predict risk of opioid overdose among Medicaid beneficiaries: A machine-learning approach
Health system data incompletely capture the social risk factors for drug overdose. This study aimed to improve the accuracy of a machine-learning algorithm to predict opioid overdose risk by integrating human services and criminal justice data with health claims data to capture the social determinan...
Autores principales: | Lo-Ciganic, Wei-Hsuan, Donohue, Julie M., Hulsey, Eric G., Barnes, Susan, Li, Yuan, Kuza, Courtney C., Yang, Qingnan, Buchanich, Jeanine, Huang, James L., Mair, Christina, Wilson, Debbie L., Gellad, Walid F. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7971495/ https://www.ncbi.nlm.nih.gov/pubmed/33735222 http://dx.doi.org/10.1371/journal.pone.0248360 |
Ejemplares similares
-
Developing and validating a machine-learning algorithm to predict opioid overdose in Medicaid beneficiaries in two US states: a prognostic modelling study
por: Lo-Ciganic, Wei-Hsuan, et al.
Publicado: (2022) -
Evaluation of Machine-Learning Algorithms for Predicting Opioid Overdose Risk Among Medicare Beneficiaries With Opioid Prescriptions
por: Lo-Ciganic, Wei-Hsuan, et al.
Publicado: (2019) -
Commentary: the importance of Medicaid expansion for criminal justice populations in the south
por: Zaller, Nickolas D., et al.
Publicado: (2017) -
Using machine learning to predict risk of incident opioid use disorder among fee-for-service Medicare beneficiaries: A prognostic study
por: Lo-Ciganic, Wei-Hsuan, et al.
Publicado: (2020) -
Medicaid Expansion’s Spillover to the Criminal Justice System: Evidence from Six Urban Counties
por: FRY, CARRIE E., et al.
Publicado: (2020)