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Predicting opioid dependence from electronic health records with machine learning.

Randall J Ellis | Zichen Wang | Nicholas Genes | Avi Ma'ayan
BioData mining | 2019

The opioid epidemic in the United States is averaging over 100 deaths per day due to overdose. The effectiveness of opioids as pain treatments, and the drug-seeking behavior of opioid addicts, leads physicians in the United States to issue over 200 million opioid prescriptions every year. To better understand the biomedical profile of opioid-dependent patients, we analyzed information from electronic health records (EHR) including lab tests, vital signs, medical procedures, prescriptions, and other data from millions of patients to predict opioid substance dependence.

Pubmed ID: 30728857

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Associated grants

  • Agency: NIH HHS, United States
    Id: OT3 OD025467
  • Agency: NIGMS NIH HHS, United States
    Id: T32 GM062754
  • Agency: NCI NIH HHS, United States
    Id: U24 CA224260
  • Agency: NCATS NIH HHS, United States
    Id: UL1 TR001433

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scikit-learn (tool)

RRID:SCR_002577

scikit-learn: machine learning in Python

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