Natural language processing for EHR-based pharmacovigilance: a structured review

Y Luo, WK Thompson, TM Herr, Z Zeng, MA Berendsen… - Drug safety, 2017 - Springer
The goal of pharmacovigilance is to detect, monitor, characterize and prevent adverse drug
events (ADEs) with pharmaceutical products. This article is a comprehensive structured …

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 …

Predicting drug-target interactions using Lasso with random forest based on evolutionary information and chemical structure

H Shi, S Liu, J Chen, X Li, Q Ma, B Yu - Genomics, 2019 - Elsevier
The identification of drug-target interactions has great significance for pharmaceutical
scientific research. Since traditional experimental methods identifying drug-target …

A curated and standardized adverse drug event resource to accelerate drug safety research

JM Banda, L Evans, RS Vanguri, NP Tatonetti… - Scientific data, 2016 - nature.com
Identification of adverse drug reactions (ADRs) during the post-marketing phase is one of
the most important goals of drug safety surveillance. Spontaneous reporting systems (SRS) …

Broad-coverage biomedical relation extraction with SemRep

H Kilicoglu, G Rosemblat, M Fiszman, D Shin - BMC bioinformatics, 2020 - Springer
Background In the era of information overload, natural language processing (NLP)
techniques are increasingly needed to support advanced biomedical information …

[HTML][HTML] Computational prediction of drug-drug interactions based on drugs functional similarities

R Ferdousi, R Safdari, Y Omidi - Journal of biomedical informatics, 2017 - Elsevier
Therapeutic activities of drugs are often influenced by co-administration of drugs that may
cause inevitable drug-drug interactions (DDIs) and inadvertent side effects. Prediction and …

Predicting potential drug-drug interactions on topological and semantic similarity features using statistical learning

A Kastrin, P Ferk, B Leskošek - PloS one, 2018 - journals.plos.org
Drug-drug interaction (DDI) is a change in the effect of a drug when patient takes another
drug. Characterizing DDIs is extremely important to avoid potential adverse drug reactions …

[HTML][HTML] An unsupervised machine learning model for discovering latent infectious diseases using social media data

S Lim, CS Tucker, S Kumara - Journal of biomedical informatics, 2017 - Elsevier
Introduction The authors of this work propose an unsupervised machine learning model that
has the ability to identify real-world latent infectious diseases by mining social media data. In …

Text mining approach to predict hospital admissions using early medical records from the emergency department

FR Lucini, FS Fogliatto, GJC da Silveira… - International journal of …, 2017 - Elsevier
Objective Emergency department (ED) overcrowding is a serious issue for hospitals. Early
information on short-term inward bed demand from patients receiving care at the ED may …

Evaluation of knowledge graph embedding approaches for drug-drug interaction prediction in realistic settings

R Celebi, H Uyar, E Yasar, O Gumus, O Dikenelli… - BMC …, 2019 - Springer
Background Current approaches to identifying drug-drug interactions (DDIs), include safety
studies during drug development and post-marketing surveillance after approval, offer …