Textual information is becoming available in abundance on the web, arising the requirement
of techniques and tools to extract the meaningful information. One of such an important …

The number of scientific publications in the literature is steadily growing, containing our
knowledge in the biomedical, health, and clinical sciences. Since there is currently no …

Text preprocessing is not only an essential step to prepare the corpus for modeling but also
a key area that directly affects the natural language processing (NLP) application results. For …

Aggregating multiple learners through an ensemble of models aim to make better
predictions by capturing the underlying distribution of the data more accurately. Different …

The amount of data collected and managed in (bio) medicine is ever-increasing. Thus, there
is a need to rapidly and efficiently collect, analyze, and characterize all this information …

The web is being loaded daily with a huge volume of data, mainly unstructured textual data,
which increases the need for information extraction and NLP systems significantly. Named …

Diversity within base classifiers has been recognized as an important characteristic of an
ensemble classifier. Data and feature sampling are two popular methods of increasing such …

A wealth of knowledge concerning relations between genes and its associated diseases is
present in biomedical literature. Mining these biological associations from literature can …

Epidemiological time series forecasting plays an important role in health public systems, due
to its ability to allow managers to develop strategic planning to avoid possible epidemics. In …

Background Recent studies have proposed deep learning techniques, namely recurrent
neural networks, to improve biomedical text mining tasks. However, these techniques rarely …