@inproceedings{97a0556d8331403f9896882be0acfacc,
title = "Prescription opioid dependence in Western New York: Using data analytics to find an answer to the opioid epidemic",
abstract = "Opioid dependence and overdose is on the rise. One indicator is the increasing trends of prescription buprenorphine use among patient on chronic pain medication. In addition to the New York State Department of Health's prescription drug monitoring programs and trainingprograms for providers and first responders to detect and treat a narcotic overdose, further examination of the population may provide important information for multidisciplinary interventions to address this epidemic. This paper uses an observational database with a Natural Language Processing (NLP) based Not Only Structured Query Language architecture to examine Electronic Health Record (EHR) data at a regional level to study the trends of prescription opioid dependence. We aim to help prioritize interventions in vulnerable population subgroups. This study provides a report of the demographic patterns of opioid dependent patients in Western New York using High Throughput Phenotyping NLP of EHR data.",
keywords = "Natural language processing, Prescriptions, Substance-related disorders",
author = "Shyamashree Sinha and Burstein, \{Gale R.\} and Leonard, \{Kenneth E.\} and Murphy, \{Timothy F.\} and Elkin, \{Peter L.\}",
note = "Publisher Copyright: {\textcopyright} 2017 International Medical Informatics Association (IMIA) and IOS Press.; 16th World Congress of Medical and Health Informatics: Precision Healthcare through Informatics, MedInfo 2017 ; Conference date: 21-08-2017 Through 25-08-2017",
year = "2017",
doi = "10.3233/978-1-61499-830-3-594",
language = "English",
series = "Studies in Health Technology and Informatics",
publisher = "IOS Press",
pages = "594--598",
editor = "Zhao Dongsheng and Gundlapalli, \{Adi V.\} and Jaulent Marie-Christine",
booktitle = "MEDINFO 2017",
}