Application of Machine Learning to Identify Risk Factors for Outpatient Opioid Prescriptions Following Spine Surgery

Notable risk factors for increased postoperative opioid use were identified, including shorter hospital stays, younger age, and prolonged operative time. These findings can help identify patients at increased risk and guide strategies to mitigate opioid dependence.

Authors

Andrew Cabrera (ORCID: https://orcid.org/0000-0002-9521-9181)
Olumide Danisa (ORCID: https://orcid.org/0000-0003-0173-7525)
David Chung (ORCID: https://orcid.org/0000-0001-5401-2048)
Alexander Bouterse (ORCID: https://orcid.org/0000-0001-5068-6635)
A Jameel

Institutions

University of California, Los Angeles (US)
Loma Linda University (US)

Publication Details

Journal: Biomedicine
Published: 2024-12-01
DOI: https://doi.org/10.37796/2211-8039.1471
Citations: 3
Primary Topic: Opioid Use Disorder Treatment
Type: article
Field-Weighted Citation Impact: 1.63

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article

Application of Machine Learning to Identify Risk Factors for Outpatient Opioid Prescriptions Following Spine Surgery

Andrew Cabrera, Olumide Danisa, David Chung, Alexander Bouterse et al.

Dec 1, 2024

3 citations

Biomedicine

Opioid Use Disorder Treatment

1.63

article

Application of Machine Learning to Identify Risk Factors for Outpatient Opioid Prescriptions Following Spine Surgery

Andrew Cabrera, Olumide Danisa, David Chung, Alexander Bouterse, A Jameel

December 1, 2024article en

3 citations

Abstract

Notable risk factors for increased postoperative opioid use were identified, including shorter hospital stays, younger age, and prolonged operative time. These findings can help identify patients at increased risk and guide strategies to mitigate opioid dependence.

BiomedicineVol. 14(4)

University of California, Los Angeles (US), Loma Linda University (US)

Zero hunger

Openalex Percentile: Top 15%

Opioid Use Disorder Treatment

1.63

DOI Open Access PDF OpenAlex

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