Predicting opioid dependence from electronic health records with machine learning.

ABSTRACT:

Background

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.

Results

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 wi

SUBMITTER: Ellis RJ 

PROVIDER: S-EPMC6352440 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature