Aiding the prescriber: developing a machine learning approach to personalized risk modeling for chronic opioid therapy amongst US Army soldiers
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DOI: 10.1007/s10729-022-09605-4
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References listed on IDEAS
- Justine S. Hastings & Mark Howison & Sarah E. Inman, 2020.
"Predicting high-risk opioid prescriptions before they are given,"
Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 117(4), pages 1917-1923, January.
- Justine S. Hastings & Mark Howison & Sarah E. Inman, 2019. "Predicting High-Risk Opioid Prescriptions Before they are Given," NBER Working Papers 25791, National Bureau of Economic Research, Inc.
- Jiaming Zeng & Berk Ustun & Cynthia Rudin, 2017. "Interpretable classification models for recidivism prediction," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(3), pages 689-722, June.
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Cited by:
- Margaret L. Brandeau, 2023. "Responding to the US opioid crisis: leveraging analytics to support decision making," Health Care Management Science, Springer, vol. 26(4), pages 599-603, December.
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Keywords
Chronic opioid therapy; Pain management; Risk score; Predictive modeling; Clinical decision support;All these keywords.
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