Skip to main navigation
Skip to search
Skip to main content
Montclair State University Home
Help & FAQ
Home
Profiles
Research units
Core Facilities
Grants/Projects
Research output
Prizes
Press/Media
Search by expertise, name or affiliation
Streamlining patients’ opioid prescription dosage: an explanatory bayesian model
Abdullah Asilkalkan
, Asli Z. Dag
,
Serhat Simsek
, Osman T. Aydas
,
Eyyub Y. Kibis
, Dursun Delen
Information Management and Business Analytics
Research output
:
Contribution to journal
›
Article
›
peer-review
3
Scopus citations
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'Streamlining patients’ opioid prescription dosage: an explanatory bayesian model'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Keyphrases
Opioid Prescription
100%
Bayesian Modeling
100%
Income Level
40%
Smoking Status
40%
Significant Predictors
20%
Web-based
20%
Surgeons
20%
Pain Assessment
20%
Genetic Algorithm
20%
Clinical Setting
20%
Nave Bayes
20%
Length of Stay
20%
Patient Gender
20%
Decision Support Tool
20%
Bayesian Network
20%
Synthetic Minority Oversampling Technique (SMOTE)
20%
Probabilistic Data
20%
Bayes Model
20%
Elastic Net Algorithm
20%
Prescription Guidelines
20%
Prescribed Opioids
20%
Smoke Effect
20%
Data-driven Framework
20%
Data Imbalance Problem
20%
Pre-discharge
20%
Illicit Opioids
20%
Opioid Dosage
20%