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Drug Prescriptions

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An Exploratory Study on Pseudo-Data Generation in Prescription and Adverse Drug Reaction Extraction.

Studies in health technology and informatics
Prescription information and adverse drug reactions (ADR) are two components of detailed medication instructions that can benefit many aspects of clinical research. Automatic extraction of this information from free-text narratives via Information Ex...

PARS, a system combining semantic technologies with multiple criteria decision aiding for supporting antibiotic prescriptions.

Journal of biomedical informatics
OBJECTIVE: Motivated by the well documented worldwide spread of adverse drug events, as well as the increased danger of antibiotic resistance (caused mainly by inappropriate prescribing and overuse), we propose a novel recommendation system for antib...

Predicting postoperative opioid use with machine learning and insurance claims in opioid-naïve patients.

American journal of surgery
BACKGROUND: The clinical impact of postoperative opioid use requires accurate prediction strategies to identify at-risk patients. We utilize preoperative claims data to predict postoperative opioid refill and new persistent use in opioid-naïve patien...

[G5 can't do without 5G].

Nederlands tijdschrift voor geneeskunde
In the Netherlands, 5 web-based systems co-exist in the public domain to provide relevant pharmacotherapeutic information for physicians, pharmacists and patients. Although these systems provide significant support to prescribers, still much can be i...

An updated, computable MEDication-Indication resource for biomedical research.

Scientific reports
The MEDication-Indication (MEDI) knowledgebase has been utilized in research with electronic health records (EHRs) since its publication in 2013. To account for new drugs and terminology updates, we rebuilt MEDI to overhaul the knowledgebase for mode...

High alert drugs screening using gradient boosting classifier.

Scientific reports
Prescription errors in high alert drugs (HAD), a group of drugs that have a high risk of complications and potential negative consequences, are a major and serious problem in medicine. Standardized hospital interventions, protocols, or guidelines wer...

Hybrid Method Incorporating a Rule-Based Approach and Deep Learning for Prescription Error Prediction.

Drug safety
INTRODUCTION: Recently, automated detection has been a new approach to address the risks posed by prescribing errors. This study focused on prescription errors and utilized real medical data to supplement the Drug Utilization Review (DUR)-based rules...