Post marketing surveillance of suspected adverse drug reactions through spontaneous reporting: current status, challenges and the future
Background: To highlight the importance of spontaneous reporting programs in post
marketing surveillance of medicines. Authors also aimed at providing various dimensions of …
marketing surveillance of medicines. Authors also aimed at providing various dimensions of …
[HTML][HTML] Artificial intelligence in the field of pharmacy practice: A literature review
Artificial intelligence (AI) is a transformative technology used in various industrial sectors
including healthcare. In pharmacy practice, AI has the potential to significantly improve …
including healthcare. In pharmacy practice, AI has the potential to significantly improve …
Knowledge graph prediction of unknown adverse drug reactions and validation in electronic health records
Unknown adverse reactions to drugs available on the market present a significant health risk
and limit accurate judgement of the cost/benefit trade-off for medications. Machine learning …
and limit accurate judgement of the cost/benefit trade-off for medications. Machine learning …
Application of machine learning in drug side effect prediction: databases, methods, and challenges
H Zhao, J Zhong, X Liang, C **e, S Wang - Frontiers of Computer Science, 2025 - Springer
Drug side effects have become paramount concerns in drug safety research, ranking as the
fourth leading cause of mortality following cardiovascular diseases, cancer, and infectious …
fourth leading cause of mortality following cardiovascular diseases, cancer, and infectious …
Facilitating prediction of adverse drug reactions by using knowledge graphs and multi-label learning models
Timely identification of adverse drug reactions (ADRs) is highly important in the domains of
public health and pharmacology. Early discovery of potential ADRs can limit their effect on …
public health and pharmacology. Early discovery of potential ADRs can limit their effect on …
A survey on adverse drug reaction studies: data, tasks and machine learning methods
Motivation Adverse drug reaction (ADR) or drug side effect studies play a crucial role in drug
discovery. Recently, with the rapid increase of both clinical and non-clinical data, machine …
discovery. Recently, with the rapid increase of both clinical and non-clinical data, machine …
Prediction of adverse drug reactions using demographic and non-clinical drug characteristics in FAERS data
The presence of adverse drug reactions (ADRs) is an ongoing public health concern. While
traditional methods to discover ADRs are very costly and limited, it is prudent to predict …
traditional methods to discover ADRs are very costly and limited, it is prudent to predict …
ADENet: a novel network-based inference method for prediction of drug adverse events
Identification of adverse drug events (ADEs) is crucial to reduce human health risks and
improve drug safety assessment. With an increasing number of biological and medical data …
improve drug safety assessment. With an increasing number of biological and medical data …
Molecular docking for prediction and interpretation of adverse drug reactions
Aim and Objective: Adverse drug reactions (ADRs) present a major burden for patients and
the healthcare industry. Various computational methods have been developed to predict …
the healthcare industry. Various computational methods have been developed to predict …
Exploring Artificial Intelligence in Healthcare: A Precise Review
Researchers and practitioners are increasingly interested in the application of artificial
intelligence (AI) to drive advancements in the pharmaceutical sector and elevate it to the …
intelligence (AI) to drive advancements in the pharmaceutical sector and elevate it to the …