To reduce adverse drug events (ADEs), hospitals need a system to support them in
monitoring ADE occurrence routinely, rapidly, and at scale. Natural language processing …

Adverse drug events (ADEs) are responsible for a significant number of hospital admissions
and fatalities. Machine learning models have been developed to assess individual patient …

Schizophrenia is a severe mental health condition that requires a long and complicated
diagnostic process. However, early diagnosis is vital to control symptoms. Deep learning …

This study attempts to categorise research conducted in the area of: use of machine learning
in healthcare, using a systematic mapping study methodology. In our attempt, we reviewed …

Background Drug discovery processes, such as new drug development, drug synergy, and
drug repurposing, consume significant yearly resources. Computer-aided drug discovery …

The accurate forecasting of harmful algal blooms (HABs) is hindered due to insufficient
monitoring frequency, numerous interacting factors, and large temporal variability in …

Artificial intelligence technologies such as machine learning and deep learning employ
techniques to anticipate results more effectively without human involvement. Since AI …

Background An adverse drug event (ADE) is any unfavorable effect that occurs due to the
use of a drug. Extracting ADEs from unstructured clinical notes is essential to biomedical text …

In recent years, machine learning methods have been rapidly implemented in the medical
domain. However, current state-of-the-art methods usually produce opaque, black-box …

Medical event prediction (MEP) is a fundamental task in the healthcare domain, which
needs to predict medical events, including medications, diagnosis codes, laboratory tests …