[HTML][HTML] On the road to explainable AI in drug-drug interactions prediction: A systematic review

TH Vo, NTK Nguyen, QH Kha, NQK Le - Computational and Structural …, 2022 - Elsevier
Over the past decade, polypharmacy instances have been common in multi-diseases
treatment. However, unwanted drug-drug interactions (DDIs) that might cause unexpected …

Artificial intelligence for drug toxicity and safety

AO Basile, A Yahi, NP Tatonetti - Trends in pharmacological sciences, 2019 - cell.com
Interventional pharmacology is one of medicine's most potent weapons against disease.
These drugs, however, can result in damaging side effects and must be closely monitored …

[PDF][PDF] Automatic hate speech detection using machine learning: A comparative study

S Abro, S Shaikh, ZH Khand, A Zafar… - International …, 2020 - pdfs.semanticscholar.org
The increasing use of social media and information sharing has given major benefits to
humanity. However, this has also given rise to a variety of challenges including the …

[HTML][HTML] Artificial intelligence in pharmaceutical sciences

M Lu, J Yin, Q Zhu, G Lin, M Mou, F Liu, Z Pan, N You… - Engineering, 2023 - Elsevier
Drug discovery and development affects various aspects of human health and dramatically
impacts the pharmaceutical market. However, investments in a new drug often go …

An ai‐based prediction model for drug‐drug interactions in osteoporosis and Paget's diseases from smiles

TNK Hung, NQK Le, NH Le, L Van Tuan… - Molecular …, 2022 - Wiley Online Library
The skeleton is one of the most important organs in the human body in assisting our motion
and activities; however, bone density attenuates gradually as we age. Among common bone …

Detection of drug–drug interactions through data mining studies using clinical sources, scientific literature and social media

S Vilar, C Friedman, G Hripcsak - Briefings in bioinformatics, 2018 - academic.oup.com
Drug–drug interactions (DDIs) constitute an important concern in drug development and
postmarketing pharmacovigilance. They are considered the cause of many adverse drug …

[HTML][HTML] Recurrent neural networks with specialized word embeddings for health-domain named-entity recognition

IJ Unanue, EZ Borzeshi, M Piccardi - Journal of biomedical informatics, 2017 - Elsevier
Background Previous state-of-the-art systems on Drug Name Recognition (DNR) and
Clinical Concept Extraction (CCE) have focused on a combination of text “feature …

The use of artificial intelligence in pharmacovigilance: a systematic review of the literature

M Salas, J Petracek, P Yalamanchili, O Aimer… - Pharmaceutical …, 2022 - Springer
Introduction Artificial intelligence through machine learning uses algorithms and prior
learnings to make predictions. Recently, there has been interest to include more artificial …

Forecasting emergency department overcrowding: A deep learning framework

F Harrou, A Dairi, F Kadri, Y Sun - Chaos, Solitons & Fractals, 2020 - Elsevier
As the demand for medical cares has considerably expanded, the issue of managing patient
flow in hospitals and especially in emergency departments (EDs) is certainly a key issue to …

Towards accurate prediction of patient length of stay at emergency department: a GAN-driven deep learning framework

F Kadri, A Dairi, F Harrou, Y Sun - Journal of Ambient Intelligence and …, 2023 - Springer
Recently, the hospital systems face a high influx of patients generated by several events,
such as seasonal flows or health crises related to epidemics (eg, COVID'19). Despite the …