  The healthcare environment is commonly referred to as "information-rich" but
also "knowledge poor". Healthcare systems collect huge amounts of data from
various sources: lab reports, medical letters, logs of medical tools or
programs, medical prescriptions, etc. These massive sets of data can provide
great knowledge and information that can improve the medical services, and
overall the healthcare domain, such as disease prediction by analyzing the
patient's symptoms or disease prevention, by facilitating the discovery of
behavioral factors for diseases. Unfortunately, only a relatively small volume
of the textual eHealth data is processed and interpreted, an important factor
being the difficulty in efficiently performing Big Data operations. In the
medical field, detecting domain-specific multi-word terms is a crucial task as
they can define an entire concept with a few words. A term can be defined as a
linguistic structure or a concept, and it is composed of one or more words with
a specific meaning to a domain. All the terms of a domain create its
terminology. This chapter offers a critical study of the current, most
performant solutions for analyzing unstructured (image and textual) eHealth
data. This study also provides a comparison of the current Natural Language
Processing and Deep Learning techniques in the eHealth context. Finally, we
examine and discuss some of the current issues, and we define a set of research
directions in this area.
