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• W2919256513 abstract "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.We trained a machine learning model to classify patients by likelihood of having a diagnosis of substance dependence using EHR data from patients diagnosed with substance dependence, along with control patients with no history of substance-related conditions, matched by age, gender, and status of HIV, hepatitis C, and sickle cell disease. The top machine learning classifier using all features achieved a mean area under the receiver operating characteristic (AUROC) curve of ~ 92%, and analysis of the model uncovered associations between basic clinical factors and substance dependence. Additionally, diagnoses, prescriptions, and procedures prior to the diagnoses of substance dependence were analyzed to elucidate the clinical profile of substance-dependent patients, relative to controls.The predictive model may hold utility for identifying patients at risk of developing dependence, risk of overdose, and opioid-seeking patients that report other symptoms in their visits to the emergency room." @default.
• W2919256513 created "2019-03-11" @default.
• W2919256513 creator A5009085275 @default.
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• W2919256513 date "2019-01-29" @default.
• W2919256513 modified "2023-10-16" @default.
• W2919256513 title "Predicting opioid dependence from electronic health records with machine learning" @default.
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• W2919256513 doi "https://doi.org/10.1186/s13040-019-0193-0" @default.
• W2919256513 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6352440" @default.
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• W2919256513 hasPublicationYear "2019" @default.
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