AIMC Topic: Drug-Related Side Effects and Adverse Reactions
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Machine Learning to Improve Decision Support for Preventing Adverse Drug Events.
Studies in health technology and informatics
May 15, 2025
One approach to preventing adverse drug events (ADEs), such as harmful drug interactions, is the implementation of clinical decision support systems (CDSS). In an ongoing project, we are investigating the accuracy of the rule-based CDSS currently uti...
[The development of model of prognostication and minimization of risk of by-effects under combined application of agents for treatment of chronic cardiac deficiency using AI].
Problemy sotsial'noi gigieny, zdravookhraneniia i istorii meditsiny
May 10, 2025
The chronic cardiac deficiency continues to be one of the leading health care problems requiring innovative solutions. The article presents mathematical algorithm to evaluate drug interactions and targeted to minimize side effects and to optimize chr...
Artificial intelligence to predict inhibitors of drug-metabolizing enzymes and transporters for safer drug design.
Expert opinion on drug discovery
May 1, 2025
INTRODUCTION: Drug-metabolizing enzymes (DMEs) and transporters (DTs) play integral roles in drug metabolism and drug-drug interactions (DDIs) which directly impact drug efficacy and safety. It is well-established that inhibition of DMEs and DTs ofte...
Development and Content Analysis Protocol for Evaluating Artificial Intelligence in Drug-Related Information.
Journal of evaluation in clinical practice
Feb 1, 2025
INTRODUCTION: Artificial intelligence (AI) has significant transformative potential across various sectors, particularly in health care. This study aims to develop a protocol for the content analysis of a method designed to assess AI applications in ...
Leveraging Generative AI for Drug Safety and Pharmacovigilance.
Current reviews in clinical and experimental pharmacology
Jan 1, 2025
Predictions are made by artificial intelligence, especially through machine learning, which uses algorithms and past knowledge. Notably, there has been an increase in interest in using artificial intelligence, particularly generative AI, in the pharm...
Machine learning for adverse event prediction in outpatient parenteral antimicrobial therapy: a scoping review.
The Journal of antimicrobial chemotherapy
Dec 2, 2024
OBJECTIVE: This study aimed to conduct a scoping review of machine learning (ML) techniques in outpatient parenteral antimicrobial therapy (OPAT) for predicting adverse outcomes and to evaluate their validation, implementation and potential barriers ...
Machine learning to predict adverse drug events based on electronic health records: a systematic review and meta-analysis.
The Journal of international medical research
Dec 1, 2024
OBJECTIVE: This systematic review aimed to provide a comprehensive overview of the application of machine learning (ML) in predicting multiple adverse drug events (ADEs) using electronic health record (EHR) data.
Machine Learning upon RDF Knowledge Graphs for Drug Safety: A Case Study on Reactome Data.
Studies in health technology and informatics
Aug 22, 2024
Artificial Intelligence (AI), particularly Machine Learning (ML), has gained attention for its potential in various domains. However, approaches integrating symbolic AI with ML on Knowledge Graphs have not gained significant focus yet. We argue that ...
Causal Deep Learning for the Detection of Adverse Drug Reactions: Drug-Induced Acute Kidney Injury as a Case Study.
Studies in health technology and informatics
Aug 22, 2024
Causal Deep/Machine Learning (CDL/CML) is an emerging Artificial Intelligence (AI) paradigm. The combination of causal inference and AI could mine explainable causal relationships between data features, providing useful insights for various applicati...
Comparing a Large Language Model with Previous Deep Learning Models on Named Entity Recognition of Adverse Drug Events.
Studies in health technology and informatics
Aug 22, 2024
The ability to fine-tune pre-trained deep learning models to learn how to process a downstream task using a large training set allow to significantly improve performances of named entity recognition. Large language models are recent models based on t...
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