Machine-learning-based adverse drug event prediction from observational health data: A review
Adverse drug events (ADEs) are responsible for a significant number of hospital admissions
and fatalities. Machine learning models have been developed to assess individual patient …
and fatalities. Machine learning models have been developed to assess individual patient …
Intelligent telehealth in pharmacovigilance: a future perspective
Pharmacovigilance improves patient safety by detecting and preventing adverse drug
events. However, challenges exist that limit adverse drug event detection, resulting in many …
events. However, challenges exist that limit adverse drug event detection, resulting in many …
The impact of transition to a digital hospital on medication errors (TIME study)
T Engstrom, E McCourt, M Canning, K Dekker… - NPJ Digital …, 2023 - nature.com
Digital transformation in healthcare improves the safety of health systems. Within our health
service, a new digital hospital has been established and two wards from a neighbouring …
service, a new digital hospital has been established and two wards from a neighbouring …
A new paradigm in adverse drug reaction reporting: consolidating the evidence for an intervention to improve reporting
Introduction Adverse drug reaction (ADR) under-reporting is highly prevalent internationally
and interventions created to address this problem have only been temporarily successful …
and interventions created to address this problem have only been temporarily successful …
[HTML][HTML] Developing a deep learning natural language processing algorithm for automated reporting of adverse drug reactions
C McMaster, J Chan, DFL Liew, E Su… - Journal of biomedical …, 2023 - Elsevier
The detection of adverse drug reactions (ADRs) is critical to our understanding of the safety
and risk-benefit profile of medications. With an incidence that has not changed over the last …
and risk-benefit profile of medications. With an incidence that has not changed over the last …
Machine learning in causal inference: Application in pharmacovigilance
Monitoring adverse drug events or pharmacovigilance has been promoted by the World
Health Organization to assure the safety of medicines through a timely and reliable …
Health Organization to assure the safety of medicines through a timely and reliable …
Machine learning risk prediction model for acute coronary syndrome and death from use of non-steroidal anti-inflammatory drugs in administrative data
Our aim was to investigate the usefulness of machine learning approaches on linked
administrative health data at the population level in predicting older patients' one-year risk of …
administrative health data at the population level in predicting older patients' one-year risk of …
Drug related adverse event assessment in neonates in clinical trials and clinical care
N Yalcin, J van den Anker… - Expert review of …, 2024 - Taylor & Francis
Introduction Assessment of drug-related adverse events is essential to fully understand the
benefit–risk balance of any drug exposure, weighing efficacy versus safety. This is needed …
benefit–risk balance of any drug exposure, weighing efficacy versus safety. This is needed …
Exploratory pharmacovigilance with machine learning in big patient data: A focused scoping review
BS Kaas‐Hansen, S Gentile, A Caioli… - Basic & clinical …, 2023 - Wiley Online Library
Background Machine learning can operationalize the rich and complex data in electronic
patient records for exploratory pharmacovigilance endeavours. Objective The objective of …
patient records for exploratory pharmacovigilance endeavours. Objective The objective of …
The use of artificial intelligence for clinical coding automation: a bibliometric analysis
In hospital settings, all information concerning the patient's diseases and medical
procedures are routinely registered in free-text format to be further abstracted and translated …
procedures are routinely registered in free-text format to be further abstracted and translated …