JOURNAL ARTICLE
Predicting opioid dependence from electronic health records with machine learning
Randall J. EllisZichen WangNicholas GenesAvi Ma’ayan
Year: 2019 Journal: BioData Mining Vol: 12 (1)Pages: 3-3 Publisher: BioMed Central
Abstract
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.
Keywords:
Health records Computer science Opioid Electronic health record Data science Machine learning Artificial intelligence Medicine Health care Political science Internal medicine
Metrics
98
Cited By
8.91
FWCI (Field Weighted Citation Impact)
33
Refs
0.98
Citation Normalized Percentile
Is in top 1%
Is in top 10%
Citation History
Topics
Machine Learning in Healthcare
Physical Sciences → Computer Science → Artificial Intelligence
Artificial Intelligence in Healthcare
Health Sciences → Health Professions → Health Information Management
Diabetes Management and Education
Health Sciences → Medicine → Endocrinology, Diabetes and Metabolism
Related Documents
JOURNAL ARTICLE
Additional file 1: of Predicting opioid dependence from electronic health records with machine learning
Ellis, RandallWang, ZichenGenes, NicholasMaâayan, Avi
Journal: OPAL (Open@LaTrobe) (La Trobe University) Year: 2019
JOURNAL ARTICLE
Additional file 7: of Predicting opioid dependence from electronic health records with machine learning
Ellis, RandallWang, ZichenGenes, NicholasMaâayan, Avi
Journal: OPAL (Open@LaTrobe) (La Trobe University) Year: 2019