Attribute supervised probabilistic dependent matrix tri-factorization model for the prediction of adverse drug-drug interaction

J Zhu, Y Liu, Y Zhang, D Li - IEEE Journal of Biomedical and …, 2020 - ieeexplore.ieee.org
Adverse drug-drug interaction (ADDI) becomes a significant threat to public health. Despite
the detection of ADDIs is experimentally implemented in the early development phase of …

DAEM: Deep attributed embedding based multi-task learning for predicting adverse drug–drug interaction

J Zhu, Y Liu, Y Zhang, Z Chen, K She, R Tong - Expert Systems with …, 2023 - Elsevier
Adverse drug–drug interaction (ADDI) is an important concern in pharmaceutical industry
and becomes a leading cause of morbidity and mortality in public health. With the increasing …

Evaluating safety and toxicity

A Bartosik, H Whittingham - The era of artificial intelligence, machine …, 2021 - Elsevier
Drug-induced toxicity poses a significant challenge for late-stage attrition of drugs. Potential
drug candidate safety profile needs to be monitored closely to avoid adverse drug reactions …

SCAN: A shared causal attention network for adverse drug reactions detection in tweets

H Kayesh, MS Islam, J Wang, R Ohira, Z Wang - Neurocomputing, 2022 - Elsevier
Twitter is a popular social media site on which people post millions of Tweets every day. As
patients often share their experiences with drugs on Twitter, Tweets can also be considered …

MTMA: Multi-task multi-attribute learning for the prediction of adverse drug–drug interaction

J Zhu, Y Liu, C Wen - Knowledge-Based Systems, 2020 - Elsevier
Adverse drug–drug interaction (ADDI) is an important issue in drug developments and
clinical applications, which causes a significant burden in the healthcare system and leads …

[HTML][HTML] Detecting high-quality signals of adverse drug-drug interactions from spontaneous reporting data

C Zhan, E Roughead, L Liu, N Pratt, J Li - Journal of Biomedical Informatics, 2020 - Elsevier
As a medicine safety issue, Drug-Drug Interaction (DDI) may become an unexpected threat
for causing Adverse Drug Events (ADEs). There is a growing demand for computational …

Identifying Drug–Drug Interactions in Spontaneous Reports Utilizing Signal Detection and Biological Plausibility Aspects

E Kontsioti, S Maskell, I Anderson… - Clinical …, 2024 - Wiley Online Library
Translational approaches can benefit post‐marketing drug safety surveillance through the
growing availability of systems pharmacology data. Here, we propose a novel Bayesian …

pADR: Towards Personalized Adverse Drug Reaction Prediction by Modeling Multi-sourced Data

J Luo, C Qian, X Wang, L Glass, F Ma - Proceedings of the 32nd ACM …, 2023 - dl.acm.org
Predicting adverse drug reactions (ADRs) of drugs is one of the most critical steps in drug
development. By pre-estimating the adverse reactions, researchers and drug development …

Diva: Exploration and validation of hypothesized drug‐drug interactions

T Kakar, X Qin, EA Rundensteiner… - Computer Graphics …, 2019 - Wiley Online Library
Adverse reactions caused by drug‐drug interactions are a major public health concern.
Currently, adverse reaction signals are detected through a tedious manual process in which …

A causality driven approach to adverse drug reactions detection in tweets

H Kayesh, MS Islam, J Wang - … Conference on Advanced Data Mining and …, 2019 - Springer
Social media sites such as Twitter is a platform where users usually express their feelings,
opinions, and experiences, eg, users often share their experiences about medications …