Prescription Opioid Dependence in Western New York: Using Data Analytics to Find an Answer to the Opioid Epidemic
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 Healths 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.
Substance-Related Disorders Prescriptions Natural Language Processing
Shyamashree Sinha Gale R Burstein Kenneth E Leonard Timothy F Murphy Peter L Elkin
Department of Biomedical Informatics,University at Buffalo-SUNY,Buffalo,New York,USA Erie County Department of Health,Buffalo,New York,USA Research Institute on Addiction;Psychology;Psychiatry,University at Buffalo-SUNY,Buffalo,New York,US Clinical and Translational Research Center,University at Buffalo-SUNY,Buffalo,New York,USA
国际会议
第十六届世界医药健康信息学大会((MEDINFO2017)、第二届世界医药健康信息学华语论坛(WCHIS 2017)、第15届全国医药信息学大会(CMIA 2017)
苏州
英文
594-598
2017-08-21（万方平台首次上网日期，不代表论文的发表时间）