AIMC Topic: Drug-Related Side Effects and Adverse Reactions

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Automation of penicillin adverse drug reaction categorisation and risk stratification with machine learning natural language processing.

International journal of medical informatics
BACKGROUND: The penicillin adverse drug reaction (ADR) label is common in electronic health records (EHRs). However, there is significant misclassification between allergy and intolerance within the EHR and most patients can be delabelled after an im...

AttentionDDI: Siamese attention-based deep learning method for drug-drug interaction predictions.

BMC bioinformatics
BACKGROUND: Drug-drug interactions (DDIs) refer to processes triggered by the administration of two or more drugs leading to side effects beyond those observed when drugs are administered by themselves. Due to the massive number of possible drug pair...

A case of fatal multidrug intoxication involving flualprazolam: distribution in body fluids and solid tissues.

Forensic toxicology
PURPOSE: Designer benzodiazepines (DBZDs) increasingly emerged on the novel psychoactive substance (NPS) market in the last few years. They are usually sold as readily available alternatives to prescription benzodiazepines (BZDs) or added to counterf...

Machine Learning Approach for Active Vaccine Safety Monitoring.

Journal of Korean medical science
BACKGROUND: Vaccine safety surveillance is important because it is related to vaccine hesitancy, which affects vaccination rate. To increase confidence in vaccination, the active monitoring of vaccine adverse events is important. For effective active...

In silico prediction of chemical-induced hematotoxicity with machine learning and deep learning methods.

Molecular diversity
Chemical-induced hematotoxicity is an important concern in the drug discovery, since it can often be fatal when it happens. It is quite useful for us to give special attention to chemicals which can cause hematotoxicity. In the present study, we focu...

Machine learning models for classification tasks related to drug safety.

Molecular diversity
In this review, we outline the current trends in the field of machine learning-driven classification studies related to ADME (absorption, distribution, metabolism and excretion) and toxicity endpoints from the past six years (2015-2021). The study fo...

Algebraic graph-assisted bidirectional transformers for molecular property prediction.

Nature communications
The ability of molecular property prediction is of great significance to drug discovery, human health, and environmental protection. Despite considerable efforts, quantitative prediction of various molecular properties remains a challenge. Although s...

Indian citizen's perspective about side effects of COVID-19 vaccine - A machine learning study.

Diabetes & metabolic syndrome
BACKGROUND AND AIMS: Ever since the vaccination drive for COVID-19 has started in India, the citizens have been sharing their views on social media about it. The present study examines the attitude of Indian citizens towards the side effects of the C...

Extracting Adverse Drug Events from Clinical Notes.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
Adverse drug events (ADEs) are unexpected incidents caused by the administration of a drug or medication. To identify and extract these events, we require information about not just the drug itself but attributes describing the drug (e.g., strength, ...

Artificial Intelligence for Unstructured Healthcare Data: Application to Coding of Patient Reporting of Adverse Drug Reactions.

Clinical pharmacology and therapeutics
Adverse drug reaction (ADR) reporting is a major component of drug safety monitoring; its input will, however, only be optimized if systems can manage to deal with its tremendous flow of information, based primarily on unstructured text fields. The a...