Today, artificial intelligence (AI) and machine learning (ML) have dramatically advanced in
various industries, especially medicine. AI describes computational programs that mimic and …

Public health practitioners and researchers have used traditional medical databases to
study and understand public health for a long time. Recently, social media data, particularly …

Introduction Artificial intelligence through machine learning uses algorithms and prior
learnings to make predictions. Recently, there has been interest to include more artificial …

Objective The primary objective of this review is to investigate the effectiveness of machine
learning and deep learning methodologies in the context of extracting adverse drug events …

Biomedical scientific literature is growing at a very rapid pace, which makes increasingly
difficult for human experts to spot the most relevant results hidden in the papers …

Traditional machine learning methods used to detect the side effects of drugs pose
significant challenges as feature engineering processes are labor-intensive, expert …

Abstract Background The Adverse Drug Event Reports (ADERs) from the spontaneous
reporting system are important data sources for studying Adverse Drug Reactions (ADRs) as …

In this study, we analyse two mobile phone activity datasets to predict the future traffic of
mobile base stations in urban areas. The predicted time series can be used to reflect the …

Objective The modern era of cognitive intelligence in clinical space has led to the rise of
'Medical Cognitive Virtual Agents'(MCVAs) which are labeled as intelligent virtual assistants …

The enormous quantity of text makes it challenging for users to obtain the key information
and knowledge. Automatic text summarization can alleviate this problem by providing …