AI Medical Compendium Journal:
Drug safety

Showing 21 to 30 of 38 articles

Artificial Intelligence and Machine Learning for Safe Medicines.

Drug safety
Authors' views on the role of artificial intelligence and machine learning in pharmacovigilance. (MP4  139807 kb).

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...

Bayesian Modeling for the Detection of Adverse Events Underreporting in Clinical Trials.

Drug safety
INTRODUCTION: Safety underreporting is a recurrent issue in clinical trials that can impact patient safety and data integrity. Clinical quality assurance (QA) practices used to detect underreporting rely on on-site audits; however, adverse events (AE...

Augmenting Product Defect Surveillance Through Web Crawling and Machine Learning in Singapore.

Drug safety
INTRODUCTION: Substandard medicines are medicines that fail to meet their quality standards and/or specifications. Substandard medicines can lead to serious safety issues affecting public health. With the increasing number of pharmaceuticals and the ...

Validating Intelligent Automation Systems in Pharmacovigilance: Insights from Good Manufacturing Practices.

Drug safety
Pharmacovigilance is the science of monitoring the effects of medicinal products to identify and evaluate potential adverse reactions and provide necessary and timely risk mitigation measures. Intelligent automation technologies have a strong potenti...

Artificial Intelligence, Real-World Automation and the Safety of Medicines.

Drug safety
Despite huge technological advances in the capabilities to capture, store, link and analyse data electronically, there has been some but limited impact on routine pharmacovigilance. We discuss emerging research in the use of artificial intelligence, ...

Application of Augmented Intelligence for Pharmacovigilance Case Seriousness Determination.

Drug safety
INTRODUCTION: Identification of adverse events and determination of their seriousness ensures timely detection of potential patient safety concerns. Adverse event seriousness is a key factor in defining reporting timelines and is often performed manu...

Transparent Reporting on Research Using Unstructured Electronic Health Record Data to Generate 'Real World' Evidence of Comparative Effectiveness and Safety.

Drug safety
Research that makes secondary use of administrative and clinical healthcare databases is increasingly influential for regulatory, reimbursement, and other healthcare decision-making. Consequently, there are numerous guidance documents on reporting fo...

Enabling Data-Driven Clinical Quality Assurance: Predicting Adverse Event Reporting in Clinical Trials Using Machine Learning.

Drug safety
INTRODUCTION: Adverse event (AE) under-reporting has been a recurrent issue raised during health authorities Good Clinical Practices (GCP) inspections and audits. Moreover, safety under-reporting poses a risk to patient safety and data integrity. The...