Over the past decade, polypharmacy instances have been common in multi-diseases
treatment. However, unwanted drug-drug interactions (DDIs) that might cause unexpected …

Abstract Foundation and Large Language Models (FLLMs) are models that are trained using
a massive amount of data with the intent to perform a variety of downstream tasks. FLLMs …

The use and production of chemical compounds are subjected to strong legislative pressure.
Chemical toxicity and adverse effects derived from exposure to chemicals are key regulatory …

Abstract Drug-Drug Interaction (DDI) prediction is one of the most critical issues in drug
development and health. Proposing appropriate computational methods for predicting …

Background Finding biomedical named entities is one of the most essential tasks in
biomedical text mining. Recently, deep learning-based approaches have been applied to …

The simultaneous administration of multiple drugs increases the probability of interaction
among them, as one drug may affect the activities of others. This interaction among drugs …

Objectives: We survey recent developments in medical Information Extraction (IE) as
reported in the literature from the past three years. Our focus is on the fundamental …

Drug–drug interactions (DDIs) are crucial for drug research and pharmacovigilance. These
interactions may cause adverse drug effects that threaten public health and patient safety …

Event extraction for the biomedical domain is more challenging than that in the general
news domain since it requires broader acquisition of domain-specific knowledge and …

Machines that can learn and correct themselves already perform better than doctors at some
tasks, says Jörg Goldhahn, but Vanessa Rampton and Giatgen A Spinas maintain that …